U.S. patent number 9,031,858 [Application Number 11/861,729] was granted by the patent office on 2015-05-12 for using biometric data for a customer to improve upsale ad cross-sale of items.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is Robert Lee Angell, James R. Kraemer. Invention is credited to Robert Lee Angell, James R. Kraemer.
United States Patent |
9,031,858 |
Angell , et al. |
May 12, 2015 |
Using biometric data for a customer to improve upsale ad cross-sale
of items
Abstract
A computer implemented method, apparatus, and computer usable
program code for generating customized marketing messages to
increase purchases by a customer. In one embodiment, an item
selected by the customer is identified to form a selected item.
Biometric readings for the customer are received from a set of
biometric devices associated with a retail facility to form the
biometric data. The biometric data is data regarding a set of
physiological responses of the customer. A set of items is selected
from a list of items associated with the selected item using the
biometric data for the customer to form a set of promoted items. A
customized marketing message for the customer is generated using a
set of personalized marketing message criteria for the customer.
The customized marketing message comprises a marketing message for
the set of promoted items.
Inventors: |
Angell; Robert Lee (Salt Lake
City, UT), Kraemer; James R. (Santa Fe, NM) |
Applicant: |
Name |
City |
State |
Country |
Type |
Angell; Robert Lee
Kraemer; James R. |
Salt Lake City
Santa Fe |
UT
NM |
US
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
39827786 |
Appl.
No.: |
11/861,729 |
Filed: |
September 26, 2007 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20080249867 A1 |
Oct 9, 2008 |
|
Related U.S. Patent Documents
|
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
11695983 |
Apr 3, 2007 |
|
|
|
|
Current U.S.
Class: |
705/14.52 |
Current CPC
Class: |
G06Q
30/06 (20130101); G06Q 30/0255 (20130101); G06Q
30/0225 (20130101); G06Q 30/0236 (20130101); G06Q
30/0271 (20130101); G06Q 30/02 (20130101); G06Q
30/0226 (20130101) |
Current International
Class: |
G06Q
30/00 (20120101) |
Field of
Search: |
;705/14 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
2247592 |
|
Mar 1992 |
|
GB |
|
2003187335 |
|
Jul 2003 |
|
JP |
|
2003263544 |
|
Sep 2003 |
|
JP |
|
0217235 |
|
Feb 2002 |
|
WO |
|
0217235 |
|
Feb 2002 |
|
WO |
|
Other References
US. Appl. No. 11/695,983, filed Apr. 3, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/861,520, filed Sep. 26, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/861,590, filed Sep. 26, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,279, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,294, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,299, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,306, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,320, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,323, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/743,982, filed May 3, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/744,024, filed May 3, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/769,409, filed Jun. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/756,198, filed May 31, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/771,252, filed Jun. 29, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/764,524, filed Jun. 18, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/861,528, filed Sep. 26, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/862,374, filed Sep. 27, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/771,887, filed Jun. 29, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/771,912, filed Jun. 29, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/861,966, filed Sep. 26, 2007, Angell et al. cited
by applicant .
U.S. Appl. No. 11/861,975, filed Sep. 26, 2007, Angell et al. cited
by applicant .
USPTO office action for U.S. Appl. No. 11/862,320 dated Aug. 5,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/743,982 dated Aug. 19,
2010. cited by applicant .
USPTO final office action for U.S. Appl. No. 11/756,198 dated Aug.
31, 2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/862,374 dated Aug. 19,
2010. cited by applicant .
USPTO final office action for U.S. Appl. No. 11/769,409 dated Aug.
31, 2010. cited by applicant .
USPTO final office action for U.S. Appl. No. 11/771,912 dated Jul.
21, 2010. cited by applicant .
USPTO final office action for U.S. Appl. No. 11/861,528 dated Sep.
9, 2010. cited by applicant .
USPTO Notice of allowance for U.S. Appl. No. 11/771,887 dated Sep.
2, 2010. cited by applicant .
Knuchel et al., "A Learning based approach for anonymous
Recommendation", Proceedings of the 8th IEEE International
Conference on E-Commerce Technology and the 3rd IEEE International
Conference on Enterprise Computing, E-Commerce and E-Services,
2006, pp. 1-8. cited by applicant .
USPTO office action for U.S. Appl. No. 11/695,983 dated Mar. 25,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/861,520 dated May 6,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/743,982 dated Mar. 24,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/769,409 dated Apr. 14,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/756,198 dated Apr. 22,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/771,252 dated May 5,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/764,524 dated Apr. 15,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/861,528 dated May 13,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/771,887 dated Mar. 8,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/771,912 dated Apr. 8,
2010. cited by applicant .
Wu et al. "Vehicle Sound Signature Recognition by Frequency Vector
Principal Component Analysis", IEEE Instrumentation and Measurement
Technology Conference, May 18-20, 1998, pp. 429-434. cited by
applicant .
Kosba, et al, "Personalized Hypermedia Presentation Techniques for
Improving Online Customer Relationships", The Knowledge Engineering
Review, Vo 16:2, 2001, pp. 111-155. cited by applicant .
Ng, Cheuk-Fan, Satisfying shoppers psychological needs: From public
market to cyber-mall, 2002, Journal of Environmental Psycology, 23
(2003) pp. 439-455. cited by applicant .
USPTO office action for U.S. Appl. No. 11/861,590 dated Jun. 15,
2010. cited by applicant .
USPTO office action for U.S. Appl. No. 11/862,306 dated Jun. 24,
2010. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,374, dated Jan.
4, 2012, 36 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/771,252, dated Oct.
15, 2010, 20 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/771,884, dated Sep. 23,
2010, 15 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/771,884, dated Mar.
18, 2011, 13 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/771,884, dated Aug. 17,
2011, 14 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/771,884, dated Feb.
28, 2012, 17 pages. cited by applicant .
Notice of Allowance regarding U.S. Appl. No. 11/771,912, dated Nov.
5, 2010, 12 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/695,983, dated Jul.
7, 2010, 23 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/861,520, dated Oct.
28, 2010, 26 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/861,590, dated Nov.
18, 2010, 31 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/861,966, dated Jul.
22, 2011, 21 pages. cited by applicant .
Notice of Allowance regarding U.S. Appl. No. 11/861,975, dated Feb.
3, 2012, 14 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,279, dated Jul.
19, 2011, 20 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/862,294, dated May 13,
2010, 19 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/862,294, dated Nov. 1,
2010, 25 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,294, dated Apr.
14, 2011, 23 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/774,884, dated Sep. 4,
2012, 16 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/861,966, dated Oct. 4,
2012, 68 pages. cited by applicant .
Final office action regarding U.S. Appl. No. 11/756,198, dated Apr.
24, 2014, 31 pages. cited by applicant .
Notice of allowance regarding U.S. Appl. No. 11/771,252, dated Mar.
25, 2014, 21 pages. cited by applicant .
Notice of allowance regarding U.S. Appl. No. 11/862,306, dated May
1, 2014, 28 pages. cited by applicant .
Final office action regarding U.S. Appl. No. 11/862,323, dated Jun.
3, 2014, 27 pages. cited by applicant .
Liraz, "Improving Your Sales Skills," Marketing Management, BizMove
Busines Guides, Feb. 1, 2001, 9 pages. cited by applicant .
Final Office Action, dated Dec. 30, 2013, regarding U.S. Appl. No.
11/862,279, 16 pages. cited by applicant .
Final Office Action, dated Feb. 11, 2014, regarding U.S. Appl. No.
11/862,306, 27 pages. cited by applicant .
Office Action, dated Dec. 3, 2013, regarding U.S. Appl. No.
11/862,320, 54 pages. cited by applicant .
Office Action, dated Jan. 28, 2014, regarding U.S. Appl. No.
11/862,323, 25 pages. cited by applicant .
Final Office Action, dated Jan. 17, 2014, regarding U.S. Appl. No.
11/695,983, 33 pages. cited by applicant .
Notice of Allowance, dated Feb. 26, 2014, regarding U.S. Appl. No.
11/862,320, 8 pages. cited by applicant .
Final Office Action, dated Feb. 26, 2014, regarding U.S. Appl. No.
11/771,252, 33 pages. cited by applicant .
Non-final office action dated Sep. 26, 2013 regarding U.S. Appl.
No. 11/756,198, 68 pages. cited by applicant .
Non-final office action dated Sep. 18, 2013 regarding U.S. Appl.
No. 11/771,252, 70 pages. cited by applicant .
Non-final office action dated Jul. 18, 2013 regarding U.S. Appl.
No. 11/862,279, 60 pages. cited by applicant .
Non-final office action dated Aug. 19, 2013 regarding U.S. Appl.
No. 11/862,323, 40 pages. cited by applicant .
Non-final office action dated Oct. 15, 2013 regarding U.S. Appl.
No. 11/862,306, 67 pages. cited by applicant .
Notice of allowance dated Sep. 13, 2013 regarding U.S. Appl. No.
11/769,409, 39 pages. cited by applicant .
Non-final office action dated Sep. 17, 2013 regarding U.S. Appl.
No. 11/695,983, 76 pages. cited by applicant .
Lyall, "What's the Buzz? Rowdy Teenagers Don't Want to Hear It,"
Barry Journal, The New York Times, Nov. 2005, 1 page. cited by
applicant .
Non-final office action dated Mar. 15, 2013 regarding U.S. Appl.
No. 11/862,323, 23 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/771,860, dated Sep. 29,
2010, 15 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/771,860, dated Mar.
1, 2011, 13 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/771,860, dated Nov. 17,
2011, 14 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/771,860, dated May
24, 2012, 14 pages. cited by applicant .
Final office action regarding U.S. Appl. No. 11/771,860, dated Mar.
28, 2013, 44 pages. cited by applicant .
Office action dated Jun. 12, 2014, regarding U.S. Appl. No.
11/455,251, 7 pages. cited by applicant .
Office action dated Oct. 23, 2014, regarding U.S. Appl. No.
11/455,251, 8 pages. cited by applicant .
Office action dated Aug. 28, 2014, regarding U.S. Appl. No.
11/862,323, 32 pages. cited by applicant .
"CRM Marketing Initiatives," In: The CRM Handbook: A Business Guide
to Customer Relationship Management, Dyche (Ed.), Addison-Wesley
Professional, Aug. 9, 2001, excerpt from
http://academic.safaribooksonline.com/print?xmlid=0-201-73062-61ch02lev1s-
ec3, downloaded Jan. 23, 2012, 13 pages. cited by applicant .
"Infogrames Brings Sense of Touch to Web Sites with Immersion
Technology," Immersion Corporation, May 22, 2000, 2 pages. cited by
applicant .
"Software Models," Excel Software,
http://web.archive.org/web/19990203054425/excelsoftware.com/models.hml,
Oct. 1996, 11 pages. cited by applicant .
"Software Prototyping," University of Houston, Sep. 22, 2008, 32
pages. cited by applicant .
Anupam et al., "Personalizing the Web Using Site Descriptions,"
Proceedings of the 10th International Workshop on Database and
Expert Systems Applications, Florence, Italy, Sep. 1-3, 1999, 7
pages. cited by applicant .
Bestavros, "Banking Industry Walks `Tightrope` in Personalization
of Web Services," Bank Systems & Technology, 37(1):54, Jan.
2000. cited by applicant .
Collins et al., "A System for Video Surveillance and Monitoring,"
Technical Report CMU-RI-TR-00-12, Robotics Institute, Carnegie
Mellon University, May 2000, 69 pages. cited by applicant .
Greiffenhagen et al., "Design, Analysis, and Engineering of Video
Monitoring Systems: An Approach and a Case Study," Proceedings of
the IEEE, 89(10):1498-1517, Oct. 2001. cited by applicant .
Hampapur et al., "Smart Video Surveillance--Exploring the Concept
of Multiscale Spatiotemporal Tracking," IEEE Signal Processing
Magazine, 22(2):38-51, Mar. 2005. cited by applicant .
Jones, "What's Your `Risk Score`?" In These Times, May 28, 2003,
http://www.inthesetimes.org/article/586/whats.sub.--your.sub.--,
accessed Feb. 12, 2011, 3 pages. cited by applicant .
Kittle, "Pilfered Profits; Both Retailers and Consumers Take a Hit
from Shoplifting," Telegraph--Herald, Dubuque, Iowa, Apr. 28, 2008,
http://proquest.umi.com/pdqweb?index=2&did=634769861&SrchMode=2&sid=5&Fmt-
=3, accessed Jul. 29, 2010, 5 pages. cited by applicant .
Knuchel et al., "A Learning-Based Hybrid Approach for Anonymous
Recommendation," 8th International Conference on E-Commerce
Technology and the 3rd IEEE International Conference on Enterprise
Computing, E-Commerce, and E-Services, San Francisco, California,
Jun. 26-29, 2006, 8 pages. cited by applicant .
Kobsa et al., "Personalised Hypermedia Presentation Techniques for
Improving Online Customer Relationships," The Knowledge Engineering
Review, 16(2):111-155, 2001. cited by applicant .
Kuhn, "Affinity Architecture: Towards a Model for Planning and
Designing Comprehensively Personalised Web Applications," Journal
of AGASI, pp. 60-63, Jul. 1999. cited by applicant .
Lipton et al., "Critical Asset Protection, Perimeter Monitoring,
and Threat Detection Using Automated Video Surveillance,"
Proceedings of the 36th Annual International Carnahan Conference on
Security Technology, Dec. 2002, pp. 1-11. cited by applicant .
Mitchell, "Computerizing Video Surveillance Techniques," IBM
Technical Disclosure Bulletin, n5 10-92, Oct. 1, 1992, 1 page.
cited by applicant .
Ng, "Satisfying Shoppers' Psychological Needs: From Public Market
to Cyber-Mall," Journal of Environmental Psychology, 23:439-455,
2003. cited by applicant .
Sandler, "Tavern Camera Mandate Proposed: Milwaukee Alderman Hopes
to Log Evidence of Misbehavior, Crime," Knight Ridder Tribune
Business News, Washington, D.C., Oct. 4, 2006,
http://proquest.umi.com/pdqweb?index=2&did=1139882851&SrchMode=2&sid=1&Fm-
t=, accessed Aug. 12, 2011, 2 pages. cited by applicant .
Wu et al., "Vehicle Sound Signature Recognition by Frequency Vector
Principal Component Analysis," IEEE Instrumentation and Measurement
Technology Conference, St. Paul, Minnesota, pp. 429-434, May 18-20,
1998. cited by applicant .
Office Action regarding U.S. Appl. No. 09/761,121, dated Mar. 3,
2004, 9 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 09/761,121, dated Nov.
24, 2004, 6 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 09/761,121, dated May 24,
2005, 8 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 09/761,121, dated Nov.
16, 2005, 8 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 09/761,121, dated Jun. 10,
2010, 10 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 09/761,121, dated Oct.
25, 2010, 8 pages. cited by applicant .
Notice of Allowance regarding U.S. Appl. No. 10/918,521, dated Sep.
27, 2006, 11 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/455,251, dated Dec. 10,
2010, 7 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/455,251, dated Apr.
27, 2011, 5 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/744,024, dated Sep. 28,
2010, 37 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/764,524, dated Aug.
19, 2010, 25 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/743,982, dated Jan.
31, 2011, 14 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,299, dated Aug.
18, 2011, 25 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,306, dated Dec.
3, 2010, 26 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/862,323, dated Sep. 3,
2010, 26 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,323, dated Aug.
19, 2011, 21 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/756,198, dated Apr. 22,
2010, 19 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/756,198, dated Aug.
31, 2010, 21 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,374, dated Jan.
28, 2011, 34 pages. cited by applicant .
Final Office Action regarding U.S. Appl. No. 11/862,374, dated May
12, 2011, 31 pages. cited by applicant .
Office Action regarding U.S. Appl. No. 11/862,374, dated Aug. 31,
2011, 37 pages. cited by applicant .
Final office action dated Nov. 20, 2014, regarding U.S. Appl. No.
11/862/323, 10 pages. cited by applicant .
Notice of Allowance dated Dec. 11, 2014, regarding U.S. Appl. No.
11/743,982, 41 pages. cited by applicant.
|
Primary Examiner: Brown; Alvin L
Attorney, Agent or Firm: Yee & Associates, P.C.
Pivnichny; John R.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of patent application
U.S. Ser. No. 11/695,983, filed Apr. 3, 2007, titled "Method and
Apparatus for Providing Customized Digital Media Marketing Content
Directly to a Customer", which is incorporated herein by
reference.
The present invention is also related to the following applications
entitled Identifying Significant Groupings of Customers for Use in
Customizing Digital Media Marketing Content Provided Directly to a
Customer, application Ser. No. 11/744,024, filed May 3, 2007;
Generating Customized Marketing Messages at a Customer Level Using
Current Events Data, application Ser. No. 11/769,409, file Jun. 24,
2007; Generating Customized Marketing Messages Using Automatically
Generated Customer Identification Data, application Ser. No.
11/756,198, filed May 31, 2007; Generating Customized Marketing
Messages for a Customer Using Dynamic Customer Behavior Data,
application Ser. No. 11/771,252, filed Jun. 29, 2007, Retail Store
Method and System, Robyn Schwartz, Publication No. US 2006/0032915
A1 (filed Aug. 12, 2004); Business Offering Content Delivery, Robyn
R. Levine, Publication No. US 2002/0111852 (filed Jan. 16, 2001)
all assigned to a common assignee, and all of which are
incorporated herein by reference.
Claims
What is claimed is:
1. A computer implemented method for generating customized
marketing messages to increase purchases by a customer, the
computer implemented method comprising: receiving external data
associated with the customer from a set of detectors located
externally to a retail facility to form external data; identifying
an item selected by the customer to form a selected item;
receiving, by a processor, biometric readings for the customer from
a set of biometric devices associated with the retail facility to
form biometric data, wherein the biometric data is data regarding a
set of physiological responses of the customer; processing the
external data with the biometric data to form dynamic data;
analyzing the dynamic data using a set of data models to identify
personalized marketing message criteria for the customer; selecting
a set of items from a list of items associated with the selected
item using the dynamic data for the customer to form a set of
promoted items; and generating a customized marketing message for
the customer using the personalized marketing message criteria and
the set of promoted items, wherein the customized marketing message
comprises a marketing message promoting a sale of the set of
promoted items.
2. The computer implemented method of claim 1 wherein the customer
is a customer in a set of customers and further comprising:
receiving data associated with the set of customers from detectors
associated with the retail facility to form detection data;
processing the detection data for the set of customers to form
grouping data for the customer; analyzing the biometric data with
the grouping data to form the dynamic data; analyzing the dynamic
data using a set of data models to identify the personalized
marketing message criteria for the customer; selecting the set of
items from the list of items associated with the selected item
using the dynamic data for the customer to form the set of promoted
items; and generating the customized marketing message for the
customer using the personalized marketing message criteria and the
set of promoted items.
3. The computer implemented method of claim 2 wherein the grouping
data identifies a grouping category for the customer, and wherein
the grouping category is selected from a group consisting of
parents with children, teenagers, children, minors unaccompanied by
adults, minors accompanied by adults, grandparents with
grandchildren, senior citizens, couples, friends, coworkers, a
customer with a pet, and a customer shopping alone, and further
comprising: identifying items in the list of items associated with
the selected item that are frequently purchased by customers in the
grouping category for the customer to form a set of frequently
purchased items; and using the biometric data to identify items in
the set of frequently purchased items to market to the customer to
form the set of promoted items.
4. The computer implemented method of claim 1 further comprising:
receiving external marketing data from a set of sources to form
current events data; processing the current events data with the
biometric data to form the dynamic data; analyzing the dynamic data
using a set of data models to identify the personalized marketing
message criteria for the customer; selecting the set of items from
the list of items associated with the selected item using the
dynamic data for the customer to form the set of promoted items;
and generating the customized marketing message for the customer
using the personalized marketing message criteria, wherein the
customized marketing message provides an incentive to purchase at
least one item in the set of promoted items.
5. The computer implemented method of claim 4 further comprising:
responsive to a determination that the current events data
indicates an event of interest to the customer occurs within a
predetermined period of time, identifying items in the list of
items associated with the selected item and the event of interest
to form a set of items of interest to the customer; and using the
biometric data to identify items in a set of items of interest to
market to the customer to form the set of promoted items.
6. The computer implemented method of claim 1 further comprising:
receiving data associated with the customer from a set of cameras
associated with the retail facility to form detection data for the
customer; processing the detection data, by a smart detection
engine, to form event data, wherein the event data describes events
associated with the customer; analyzing the event data to identify
patterns of events to form customer behavior data; processing the
customer behavior data with the biometric data to form the dynamic
data; analyzing the dynamic data using a set of data models to
identify the personalized marketing message criteria for the
customer; identifying items in the list of items associated with
the selected item using the dynamic data to form the set of
promoted items, wherein the customer behavior data and the
biometric data indicates an interest of the customer in receiving
marketing messages associated with at least one item in the set of
promoted items; and generating the customized marketing message for
the customer using the personalized marketing message criteria and
the set of promoted items.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention is related generally to an improved data
processing system and in particular to a method and apparatus for
processing video and audio data. More particularly, the present
invention is directed to a computer implemented method, apparatus,
and computer usable program code for using biometric data for a
customer to generate customized marketing messages promoting
upsales and cross-sales of items.
2. Description of the Related Art
When a customer shows interest in purchasing a particular item,
merchants frequently attempt to induce the customer to purchase a
more expensive brand of the item, an upgraded version of the item,
a larger and more expensive size of the item, and/or other
additions and special features for the item to make the sale more
profitable. These sales techniques are sometimes referred to as
upselling or upsale. For example, if a user is interested in
purchasing a used car, the salesman may attempt to induce the
customer into purchasing a more expensive new car instead. If the
salesman is successful, the upsale of the more expensive car will
likely generate greater profit and/or greater revenue.
Another sales technique involves selling related products to
customers to increase profit and/or revenue. For example, if a
customer shows interest in purchasing a bicycle, the salesman may
attempt to induce the customer into purchasing a bicycle helmet, a
bicycle tire pump, a spare tire, an extra bicycle chain, and/or
other items that might be used in conjunction with the bicycle.
This sales technique is referred to as cross-selling.
In the past, merchants, such as store owners and operators,
frequently had a personal relationship with their customers. The
merchant often knew their customers' names, address, marital
status, ages of their children, hobbies, place of employment,
anniversaries, birthdays, likes, dislikes and personal preferences.
The merchant was able to use this information to cater to customer
needs and push upsales and cross-sales of items the customer might
be likely to purchase based on the customer's personal situation
and the merchant's personal knowledge of purchases by his
customers.
However, with the continued growth of large cities, the
corresponding disappearance of small, rural towns, and the
increasing number of large, impersonal chain stores with multiple
employees, the merchants and employees of retail businesses rarely
recognize regular customers, and almost never know the customer's
name or any other details regarding their customer's personal
preferences that might assist the merchant or employee in marketing
efforts directed toward a particular customer.
One solution to this problem is directed toward using profile data
for a customer to generate marketing messages that may be sent to
the customer by email, print media, telephone, or over the World
Wide Web via a web page. Customer profile data typically includes
information provided by the customer in response to a questionnaire
or survey, such as name, address, telephone number, gender, and
indicators of particular products the customer is interested in
purchasing. Demographic data regarding a customer's age, sex,
income, career, interests, hobbies, and consumer preferences may
also be included in customer profile data.
Advertising computers can generate a customer advertisement based
on the customer's static profile. However, this method only
provides a small number of pre-generated advertisements that are
directed towards a fairly large segment of the population rather
than to one individual.
In another solution, user profile data, demographic data, point of
contact data, and transaction data are analyzed to generate
advertising content for customers that target the information
content presented to individual consumers or users to increase the
likelihood that the customer will purchase the goods or services
presented. Current solutions do not utilize all of the potential
customer data elements that may be available to a retail owner or
operator for generating customized marketing messages targeted to
individual customers. Other data pieces are needed to provide
effective dynamic one-to-one marketing of messages to the potential
customer. Therefore, the data elements in prior art only provides
approximately seventy-five percent (75%) of the needed data.
SUMMARY OF THE INVENTION
The illustrative embodiments provide a computer implemented method,
apparatus, and computer usable program code for generating
customized marketing messages to increase purchases by a customer.
In one embodiment, an item selected by the customer is identified
to form a selected item. Biometric readings for the customer are
received from a set of biometric devices associated with a retail
facility to form the biometric data. The biometric data is data
regarding a set of physiological responses of the customer. A set
of items is selected from a list of items associated with the
selected item using the biometric data for the customer to form a
set of promoted items. A customized marketing message for the
customer is generated using a set of personalized marketing message
criteria for the customer. The customized marketing message
comprises a marketing message promoting a sale of the set of
promoted items.
BRIEF DESCRIPTION OF THE DRAWINGS
The novel features believed characteristic of the invention are set
forth in the appended claims. The invention itself, however, as
well as a preferred mode of use, further objectives and advantages
thereof, will best be understood by reference to the following
detailed description of an illustrative embodiment when read in
conjunction with the accompanying drawings, wherein:
FIG. 1 is a pictorial representation of a network of data
processing systems in which illustrative embodiments may be
implemented;
FIG. 2 is a block diagram of a digital customer marketing
environment in which illustrative embodiments may be
implemented;
FIG. 3 is a block diagram of a data processing system in which
illustrative embodiments may be implemented;
FIG. 4 is a diagram of a display device in the form of a personal
digital assistant (PDA) in accordance with a preferred embodiment
of the present invention;
FIG. 5 is a block diagram of a personal digital assistant display
device in accordance with a preferred embodiment of the present
invention;
FIG. 6 is a block diagram of a data processing system for analyzing
biometric data for use in generating customized marketing messages
that promote upsale and cross-sale of items in accordance with an
illustrative embodiment;
FIG. 7 is a block diagram of a dynamic marketing message assembly
transmitting a customized marketing message to a set of display
devices in accordance with an illustrative embodiment;
FIG. 8 is a block diagram of an identification tag reader for
gathering data associated with one or more items in accordance with
an illustrative embodiment;
FIG. 9 is a block diagram illustrating an external marketing
manager for generating current events data in accordance with an
illustrative embodiment;
FIG. 10 is a block diagram illustrating a smart detection engine
for generating dynamic data in accordance with an illustrative
embodiment;
FIG. 11 is a block diagram of a shopping container in accordance
with an illustrative embodiment;
FIG. 12 is a block diagram of a shelf in a retail facility in
accordance with an illustrative embodiment;
FIG. 13 is a block diagram illustrating a list of correlated items
for promoting cross sales of related items in accordance with an
illustrative embodiment;
FIG. 14 is a block diagram illustrating a list of upsale items
corresponding to selected items in accordance with an illustrative
embodiment;
FIG. 15 is a flowchart illustrating a process for generating a
customized marketing message for promoting cross sales of items
related to an item selected by a customer in accordance with an
illustrative embodiment;
FIG. 16 is a flowchart illustrating a process for generating a list
of items purchased in correlation with a selected item in
accordance with an illustrative embodiment;
FIG. 17 is a flowchart illustrating a process for generating a
customized marketing message for promoting upsales of items in
accordance with an illustrative embodiment;
FIG. 18 is a flowchart illustrating a process for monitoring for a
change in biometric readings associated with a customer in
accordance with an illustrative embodiment; and
FIG. 19 is a flowchart illustrating a process for generating a
customized marketing message cross-sales and upsales of items using
dynamic data in accordance with an illustrative embodiment.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
With reference now to the figures and in particular with reference
to FIGS. 1-5, exemplary diagrams of data processing environments
are provided in which illustrative embodiments may be implemented.
It should be appreciated that FIGS. 1-5 are only exemplary and are
not intended to assert or imply any limitation with regard to the
environments in which different embodiments may be implemented.
Many modifications to the depicted environments may be made.
With reference now to the figures, FIG. 1 depicts a pictorial
representation of a network of data processing systems in which
illustrative embodiments may be implemented. Network data
processing system 100 is a network of computers in which
embodiments may be implemented. Network data processing system 100
contains network 102, which is the medium used to provide
communications links between various devices and computers
connected together within network data processing system 100.
Network 102 may include connections, such as wire, wireless
communication links, or fiber optic cables.
In the depicted example, server 104 and server 106 connect to
network 102 along with storage area network (SAN) 108. Storage area
network 108 is a network connecting one or more data storage
devices to one or more servers, such as servers 104 and 106. A data
storage device, may include, but is not limited to, tape libraries,
disk array controllers, tape drives, flash memory, a hard disk,
and/or any other type of storage device for storing data. Storage
area network 108 allows a computing device, such as client 110 to
connect to a remote data storage device over a network for block
level input/output.
In addition, clients 110 and 112 connect to network 102. These
clients 110 and 112 may be, for example, personal computers or
network computers. In the depicted example, server 104 provides
data, such as boot files, operating system images, and applications
to clients 110 and 112. Clients 110 and 112 are clients to server
104 in this example.
Digital customer marketing environment 114 also connects to network
102. Digital customer marketing environment 114 is a marketing
environment in which a customer may view, select order, and/or
purchase one or more items. Digital customer marketing environment
114 may include one or more facilities, buildings, or other
structures for wholly or partially containing the items. A facility
may include, but is not limited to, a grocery store, a clothing
store, a marketplace, a retail department store, a convention
center, or any other type of structure for housing, storing,
displaying, and/or selling items.
Items in digital customer marketing environment 114 may include,
but are not limited to, comestibles, clothing, shoes, toys,
cleaning products, household items, machines, any type of
manufactured items, entertainment and/or educational materials, as
well as entrance or admittance to attend or receive an educational
or entertainment service, activity, or event. Items for purchase
could also include services, such as ordering dry cleaning
services, food delivery, or any other services.
Comestibles include solid, liquid, and/or semi-solid food and
beverage items. Comestibles may be, but are not limited to, meat
products, dairy products, fruits, vegetables, bread, pasta,
pre-prepared or ready-to-eat items, as well as unprepared or
uncooked food and/or beverage items. For example, a comestible
could include, without limitation, a box of cereal, a steak, tea
bags, a cup of tea that is ready to drink, popcorn, pizza, candy,
or any other edible food or beverage items.
An entertainment or educational activity, event, or service may
include, but is not limited to, a sporting event, a music concert,
a seminar, a convention, a movie, a ride, a game, a theatrical
performance, and/or any other performance, show, or spectacle for
entertainment or education of customers. For example, entertainment
or educational activity or event could include, without limitation,
the purchase of seating at a football game, purchase of a ride on a
roller coaster, purchase of a manicure, or purchase of admission to
view a film.
Digital customer marketing environment 114 may also includes a
parking facility for parking cars, trucks, motorcycles, bicycles,
or other vehicles for conveying customers to and from digital
customer marketing environment 114. A parking facility may include
an open air parking lot, an underground parking garage, an above
ground parking garage, an automated parking garage, and/or any
other area designated for parking customer vehicles.
For example, digital customer marketing environment 114 may be, but
is not limited to, a grocery store, a retail store, a department
store, an indoor mall, an outdoor mall, a combination of indoor and
outdoor retail areas, a farmer's market, a convention center, a
sports arena or stadium, an airport, a bus depot, a train station,
a marina, a hotel, fair grounds, an amusement park, a water park,
and/or a zoo.
Digital customer marketing environment 114 encompasses a range or
area in which marketing messages may be transmitted to a digital
display device for presentation to a customer within digital
customer marketing environment. Digital multimedia management
software is used to manage and/or enable generation, management,
transmission, and/or display of marketing messages within digital
customer marketing environment. Examples of digital multimedia
management software include, but are not limited to, Scala.RTM.
digital media/digital signage software, EK3.RTM. digital
media/digital signage software, and/or Allure digital media
software.
In this example, digital customer marketing environment 114 is
connected to server 104 and server 106 via network 102. In another
embodiment, digital customer marketing environment 114 includes one
or more servers located on-site at digital customer marketing
environment. In this example, network 102 is optional. In other
words, if one or more servers and/or data processing systems are
located at digital customer marketing environment 114, the
illustrative embodiments are capable of being implemented without a
network connection.
In the depicted example, network data processing system 100 is the
Internet with network 102 representing a worldwide collection of
networks and gateways that use the Transmission Control
Protocol/Internet Protocol (TCP/IP) suite of protocols to
communicate with one another. At the heart of the Internet is a
backbone of high-speed data communication lines between major nodes
or host computers, consisting of thousands of commercial,
governmental, educational and other computer systems that route
data and messages. Of course, network data processing system 100
also may be implemented as a number of different types of networks,
such as, without limitation, an intranet, an Ethernet, a local area
network (LAN), and/or a wide area network (WAN).
Network data processing system 100 may also include additional data
storage devices, such as, without limitation, a hard disk, a
compact disk (CD), a compact disk rewritable (CD-RW), a flash
memory, a compact disk read-only memory (CD ROM), a non-volatile
random access memory (NV-RAM), and/or any other type of storage
device for storing data
FIG. 1 is intended as an example, and not as an architectural
limitation for different embodiments. Network data processing
system 100 may include additional servers, clients, data storage
devices, and/or other devices not shown. For example, server 104
may also include devices not depicted in FIG. 1, such as, without
limitation, a local data storage device. A local data storage
device could include a hard disk, a flash memory, a non-volatile
random access memory (NVRAM), a read only memory (ROM), and/or any
other type of device for storing data.
A merchant, owner, operator, manager or other employee associated
with digital customer marketing environment 114 typically wants to
market upsale items or related cross-sale products or services to a
customer or potential customer in the most convenient and efficient
manner possible so as to maximize resulting purchases of goods
and/or services by the customer and increase revenue. Therefore,
the aspects of the illustrative embodiments recognize that it is
advantageous for the merchant to have as much information regarding
a customer as possible to identify which items are most likely or
expected to be purchased by the customer, and therefore, the best
candidates for marketing to the customer and personalize the
merchant's marketing strategy to that particular customer.
In addition, customers generally prefer to only receive marketing
messages that are relevant to that particular customer. For
example, a single college student with no children would typically
not be interested in marketing messages offering sale prices or
incentives for purchasing baby diapers or children's toys. In
addition, that college student would not want to waste their time
viewing such marketing messages. Likewise, a customer that is a
non-smoker may be inconvenienced by being presented with
advertisements, email, digital messages, or other marketing
messages for tobacco products.
Therefore, the illustrative embodiments provide a computer
implemented method, apparatus, and computer usable program code for
generating customized marketing messages to increase purchases by a
customer. In one embodiment, an item selected by the customer is
identified to form a selected item. Biometric readings for the
customer are received from a set of biometric devices associated
with a retail facility to form the biometric data. As used herein,
the term "set" includes one or more.
The biometric data is data regarding a set of physiological
responses of the customer. A set of items is selected from a list
of items associated with the selected item using the biometric data
for the customer to form a set of promoted items. A set of promoted
items includes a single promoted item, as well as two or more
promoted items. A customized marketing message for the customer is
generated using a set of personalized marketing message criteria
for the customer. The customized marketing message comprises a
marketing message for the set of promoted items.
In one embodiment, the list of items associated with the selected
items is a list of upsale items. An upsale item in the set of
promoted items is an item that provides a same basic functionality
as the selected item. In this example, the customized marketing
message prompts the customer to purchase the upsale item instead of
the selected item because a sale of the upsale item to the customer
produces a greater amount of revenue or a greater amount of profit
than a sale of the selected item.
In another embodiment, the list of items associated with the
selected items is a list of correlated items. A correlated item
provides a different basic functionality than the selected item.
The process identifies a set of items in a list of correlated items
to form the set of promoted items. The customized marketing message
prompts the customer to purchase the correlated item in the set of
promoted items in addition to the selected item.
The customized marketing message is a marketing message that is
generated for a particular customer or group of customers based on
one or more personalized message criteria for the customer. In
other words, the customized marketing message is a highly
personalized marketing message for a specific or particular
customer. The personalized marketing message may include special
offers or incentives to a particular customer. An incentive is an
offer of a discount or reward to encourage a customer to select,
order, and/or purchase one or more items.
The customized marketing message is more than just a marketing
message that includes the customer's name or address. The
customized marketing message presents a marketing message pushing
the sale of an item that is selected and generated dynamically in
real-time as the customer is shopping in the store.
FIG. 2 is a block diagram of a digital customer marketing
environment in which illustrative embodiments may be implemented.
Digital customer marketing environment 200 is a marketing
environment, such as digital customer marketing environment 114 in
FIG. 1.
Retail facility 202 is a retail facility for wholly or partially
storing, enclosing, or displaying items for marketing, viewing,
selection, order, and/or purchase by a customer. For example,
retail facility 202 may be, without limitation, a retail store,
supermarket, book store, clothing store, or shopping mall. However,
retail facility 202 is not limited to retail stores. For example,
retail facility 202 may also include, without limitation, a sports
arena, amusement park, water park, convention center, trade center,
or any other facility for offering, providing, or displaying items
for sale. In this example, retail facility 202 is a grocery store
or a department store.
Detectors 204-210 are devices for gathering data associated with a
set of customers. A set of customers is a set of one or more
customers. Detectors 204-210 are examples of detectors that are
located externally to retail facility 202. In this example,
detectors 204-210 are located at locations along an outer perimeter
of digital customer marketing environment 200. However, detectors
204-210 may be located at any position within digital customer
marketing environment 200 that is outside retail facility 202 to
detect customers before the customers enter retail facility 202
and/or after customers leave digital customer marketing environment
200.
Detectors 204-210 may be any type of detecting devices for
gathering dynamic data associated with a customer located outside
of retail facility 202, including, but not limited to, a camera, a
set of one or more motion sensor devices, a sonar, microphone,
sound recording device, audio detection device, a voice recognition
system, a heat sensor, a seismograph, a pressure sensor, a device
for detecting odors, scents, and/or fragrances, a radio frequency
identification (RFID) tag reader, a global positioning system (GPS)
receiver, and/or any other detection device for detecting a
presence of a human, animal, and/or vehicle outside of the retail
facility. A vehicle is any type of vehicle for conveying people,
animals, or objects to a destination. A vehicle may include, but is
not limited to, a car, bus, truck, motorcycle, boat, airplane, or
any other type of vehicle.
A heat sensor is any known or available device for detecting heat,
such as, but not limited to, a thermal imaging device for
generating images showing thermal heat patterns. A heat sensor can
detect body heat generated by a human or animal and/or heat
generated by a vehicle, such as an automobile or a motorcycle. A
set of heat sensors may include one or more heat sensors.
A motion detector may include any type of known or available motion
detector device. A motion detector device may include, but is not
limited to, a motion detector device using a photo-sensor, radar or
microwave radio detector, or ultrasonic sound waves.
A motion detector using ultrasonic sound waves transmits or emits
ultrasonic sound waves. The motion detector detects or measures the
ultrasonic sound waves that are reflected back to the motion
detector. If a human, animal, or other object moves within the
range of the ultrasonic sound waves generated by the motion
detector, the motion detector detects a change in the echo of sound
waves reflected back. This change in the echo indicates the
presence of a human, animal, or other object moving within the
range of the motion detector.
In one example, a motion detector device using a radar or microwave
radio detector may detect motion by sending out a burst of
microwave radio energy and detecting the same microwave radio waves
when the radio waves are deflected back to the motion detector. If
a human, animal, or other object moves into the range of the
microwave radio energy field generated by the motion detector, the
amount of energy reflected back to the motion detector is changed.
The motion detector identifies this change in reflected energy as
an indication of the presence of a human, animal, or other object
moving within the motion detectors range.
A motion detector device, using a photo-sensor, detects motion by
sending a beam of light across a space into a photo-sensor. The
photo-sensor detects when a human, animal, or object breaks or
interrupts the beam of light as the human, animal, or object by
moving in-between the source of the beam of light and the
photo-sensor. These examples of motion detectors are presented for
illustrative purposes only. A motion detector in accordance with
the illustrative embodiments may include any type of known or
available motion detector and is not limited to the motion
detectors described herein.
A pressure sensor detector may be, for example, a device for
detecting a change in weight or mass associated with the pressure
sensor. For example, if one or more pressure sensors are imbedded
in a sidewalk, Astroturf, or floor mat, the pressure sensor detects
a change in weight or mass when a human customer or animal steps on
the pressure sensor. The pressure sensor may also detect when a
human customer or animal steps off of the pressure sensor. In
another example, one or more pressure sensors are embedded in a
parking lot, and the pressure sensors detect a weight and/or mass
associated with a vehicle when the vehicle is in contact with the
pressure sensor. A vehicle may be in contact with one or more
pressure sensors when the vehicle is driving over one or more
pressure sensors and/or when a vehicle is parked on top of one or
more pressure sensors.
A camera may be any type of known or available camera, including,
but not limited to, a video camera for taking moving video images,
a digital camera capable of taking still pictures and/or a
continuous video stream, a stereo camera, a web camera, and/or any
other imaging device capable of capturing a view of whatever
appears within the camera's range for remote monitoring, viewing,
or recording of a distant or obscured person, object, or area.
Various lenses, filters, and other optical devices such as zoom
lenses, wide angle lenses, mirrors, prisms and the like may also be
used with an image capture device to assist in capturing the
desired view. The image capture device may be fixed in a particular
orientation and configuration, or it may, along with any optical
devices, be programmable in orientation, light sensitivity level,
focus or other parameters. Programming data may be provided via a
computing device, such as server 104 in FIG. 1.
An image capture device may be, without limitation, one or more
cameras. A camera may also be a stationary camera and/or
non-stationary cameras. A non-stationary camera is a camera that is
capable of moving and/or rotating along one or more directions,
such as up, down, left, right, and/or rotate about an axis of
rotation. The camera may also be capable of moving to follow or
track a person, animal, or object in motion. In other words, the
camera may be capable of moving about an axis of rotation in order
to keep a customer, animal, or object within a viewing range of the
camera lens. In this example, detectors 204-210 are non-stationary
digital video cameras.
Detectors 204-210 are connected to an analysis server on a data
processing system, such as network data processing system 100 in
FIG. 1. The analysis server is illustrated and described in greater
detail in FIG. 6 below. The analysis server includes software for
analyzing digital images and other data captured by detectors
204-210 to track and/or visually identify retail items, containers,
and/or customers outside retail facility 202. Attachment of
identifying marks may be part of this visual identification in the
illustrative embodiments.
The analysis server analyzes and/or processes the data captured by
detectors 204-210 to form dynamic data. Dynamic data is data
associated with a customer that is generated in real-time as a
customer is shopping at a retail facility. Real-time refers to
something that occurs immediately as or within some period of time
needed to achieve an objective.
As used herein, data associated with a customer may include data
regarding the customer, members of the customer's family, pets,
cars or other vehicles, the customer's shopping companions, the
customer's friends, and/or any other data pertaining to the
customer. The customized marketing message is delivered to a
display device associated with the customer for display.
Dynamic data is data for a customer that is gathered and processed
in real time as a customer is shopping or browsing in digital
customer marketing environment 114. Processing dynamic data may
include, but is not limited to, formatting the dynamic data for
utilization and/or analysis in one or more data models, combining
the dynamic data with external data and/or static customer data,
comparing the dynamic data to a data model and/or filtering the
dynamic data for relevant data elements.
Dynamic data is processed or filtered for analysis in a set of one
or more data models. For example, if the dynamic data includes
video images of a customer inside a retail facility, the video
images may need to be processed to convert the video images into
data and/or metadata for analysis in one or more data models. For
example, a data model may not be capable of analyzing raw, or
unprocessed video images captured by a camera. The video images may
need to be processed into data and/or meta data describing the
contents of the video images before a data model may be used to
organize, structure, or otherwise manipulate data and/or metadata.
The video images converted to data and/or meta data that is ready
for processing or analysis in a set of data models is an example of
processed dynamic data.
The dynamic data is analyzed using a set of data models to identify
and create specific and personalized marketing message criteria for
the customer. A set of data models includes one or more data
models. A data model is a model for structuring, defining,
organizing, imposing limitations or constraints, and/or otherwise
manipulating data and metadata to produce a result. A data model
may be generated using any type of modeling method or simulation
including, but not limited to, a statistical method, a data mining
method, a causal model, a mathematical model, a marketing model, a
behavioral model, a psychological model, a sociological model, or a
simulation model.
The dynamic data may be analyzed in a single data model or in a
series of data models. For example, and without limitation, a first
data model in a series of data models is used to analyze the
dynamic data. The output results of analyzing the dynamic data in
the first data model is entered into a second data model as input.
The output of the second data model is then entered into a third
data model as input for analysis. This process can continue until
the dynamic data has been analyzed in any number of data models in
the set of data models. In another example, the dynamic data is
analyzed in parallel in two or more data models in the set of data
models. The results output by the two or more data models are used
to generate the customized marketing message and/or identify upsale
and/or cross-sale items to be marketed to the customer.
In the embodiments described herein, dynamic data includes, but is
not limited to, external data, grouping data, current events data,
identification data, and/or customer behavior data. Thus, dynamic
data can be only external data, external data and grouping data,
external data, grouping data, current events data, identification
data, and/or customer behavior data, or any other combination of
these types of dynamic data.
External data is data regarding detection of a customer's presence
outside a retail facility, a detection of a customer outside the
retail facility that is moving toward an entrance to the retail
facility indicating that the customer is about to go inside the
facility, and/or detection of a customer exiting the retail
facility. The external data may also indicate detection of a
presence of a customer's vehicle, such as a car, bicycle,
motorcycle, bus, or truck. External data may also include, without
limitation, grouping data, identification data, and/or customer
behavior data.
The external data is gathered by detectors 204-210. Thus, the
external data includes, without limitation, video images, sound
recorded by a microphone or other sound recording device, pressure
sensor data gathered by one or more pressure sensors, data received
from heat sensors, radio frequency identification tag signals
recognized by a radio frequency identification tag reader, and/or
any other type of detection data.
In this example, four detectors, detectors 204-210, are located
outside retail facility 202. However, any number of detectors may
be used to detect, track, and/or gather dynamic data associated
with customers outside retail facility 202. For example, a single
detector, as well as two or more detectors may be used outside
retail facility 202 for tracking customers entering and/or exiting
retail facility 202.
Retail facility 202 may also optionally include set of detectors
212 inside retail facility 202. Set of detectors 212 is a set of
one or more detectors, such as detectors 204-210, for gathering
dynamic data inside retail facility 202. The dynamic data gathered
by set of detectors 212 includes, without limitation, grouping
data, identification data, and/or customer behavior data.
Grouping data is data regarding a grouping category for a customer.
A grouping category describes the relationship of a group or subset
of customers. A grouping category includes, without limitation,
parents with children, teenagers, children, minors unaccompanied by
adults, minors accompanied by adults, grandparents with
grandchildren, senior citizens, couples, friends, coworkers, a
customer shopping alone, a customer accompanied by one or more
pets, such as a dog, or any other category for a customer.
Grouping data is generated using either external data or detection
data gathered inside a retail facility. Detection data gathered
inside the retail facility includes, but is not limited to, video
images of a customer captured by cameras located inside or
internally to a retail facility and/or data regarding the current
or real-time contents of a customer's shopping basket gathered by a
set of radio frequency identification sensors located inside the
retail facility.
Identification data is data identifying a customer or a customer's
vehicle. Identification data may be generated by using facial
recognition technology to analyze camera images and identify
customers. Video images of a customer's car may also be analyzed to
identify the car's license plate, make, model, year, color, and/or
other attributes of the vehicle which may be used to identify the
vehicle. The identification of the vehicle can then be used to
identify the customer that owns and/or drives the vehicle.
Identification data is generated using either external data
gathered outside the retail facility or detection data gathered
inside the retail facility.
Current events data is data describing events, news items,
holidays, event days when an event is scheduled to take place, and
competitor marketing data. An event may be any type of event,
including, without limitation, parades, sports events, conventions,
shows, theater and movie show times, concerts, opera performances,
and circus performances. An event may also be a holiday or other
significant date. Holidays may be days like Christmas,
Thanksgiving, Earth Day, Memorial Day, Easter, Election Day, or any
other day. A significant date may include, without limitation, the
customer's birthday, anniversary, children's birthdays, birthdays
and anniversary of family and friends, the first day of school, the
first day of summer vacation, or any other significant dates.
Competitor marketing data includes, without limitation, data
describing competitor prices, sales, discounts on items, rebates,
special offers, incentives, give-a-ways, free food, competitor
store locations, competitor store hours of operation, competitor
store openings, competitor store close-out sales or going out of
business sales, competitor inventory, and/or any other available
data regarding competitor marketing.
Customer behavior data is data describing a pattern of events
associated with the customer. Customer behavior data includes,
without limitation, data describing, locations in the retail
facility where the customer has walked, the pace or speed at which
the customer is walking, the amount of time the customer browses
for items on a shelf before selecting an item and placing the item
in the customer's shopping basket or cart, and/or the rate at which
the customer selects items for purchase over time. Customer
behavior data is generated using either external data gathered
outside the retail facility or detection data gathered inside the
retail facility.
Set of detectors 212 may be located at any location within retail
facility 202. In addition, set of detectors 212 may include
multiple detectors located at differing locations within retail
facility 202. For example, a detector in set of detectors 212 may
be located, without limitation, at an entrance to retail facility
202, on one or more shelves in retail facility 202, and/or on one
or more doors and/or doorways in retail facility 202.
For example, set of detectors 212 may include one or more cameras
or other image capture devices located inside retail facility 202
for tracking and/or identifying items, containers for items,
shopping containers and shopping carts, and/or customers inside
retail facility 202 to form internal data. The camera or other
detector in set of detectors 212 may be coupled to and/or in
communication with the analysis server. In addition, more than one
image capture device may be operated simultaneously without
departing from the illustrative embodiments of the present
invention.
Display devices 214 are multimedia devices for displaying
customized marketing messages to customers. Display devices 214 may
be any type of display device for presenting a text, graphic,
audio, video, and/or any combination of text, graphics, audio, and
video to a customer. In this example, display devices 214 are
located inside retail facility 202. Display devices 214 include one
or more display devices inside retail facility 202 for use and/or
viewing by at least one customer.
Display devices 216 include one or more display devices located
outside retail facility 202, such as, without limitation, display
devices located in a parking lot, queue line, and/or other area
outside of retail facility 202. A display device may be implemented
as a device such as, without limitation, a personal digital
assistant (PDA), a cellular telephone with a display screen, an
electronic sign, a laptop computer, a tablet PC, a kiosk, a digital
media display, a digital message board, a monitor, a smart watch, a
display screen mounted on a shopping container, and/or any other
type of device for displaying digital messages to a customer. The
display device may optionally include a printer for printing the
customized marketing message on a paper medium. Display devices 216
may be used in the absence of display devices 214 inside retail
facility 202 or in addition to display devices 214 located inside
retail facility 202.
Biometric devices 218 include one or more devices for measuring
and/or detecting a change in biometric readings of a customer that
exceeds a threshold or baseline change in biometric readings.
Biometric readings may include a measurement of a customer's heart
rate over a given period of time, blood pressure, voice stress,
skin temperature data, body temperature data, pupil dilation,
fingerprint data, respiration, and/or an amount of perspiration. If
biometric devices 218 measures a change in biometric readings for a
customer that exceeds a predefined threshold change that
corresponds to a customer viewing an item and/or a marketing
message, the change in the customer's biometric readings may be
attributed to the item and/or the marketing message.
Container 220 is a container for holding, carrying, transporting,
or moving one or more items. For example, container 220 may be,
without limitation, a shopping cart, a shopping bag, a shopping
basket, and/or any other type of container for holding items. In
this example, container 220 is a shopping cart.
In this example in FIG. 2, only one container 220 is depicted
inside retail facility 202. However, any number of containers may
be used inside and/or outside retail facility 202 for holding,
carrying, transporting, or moving items selected by customers.
Container 220 may optionally include biometric device 222.
Biometric device 222 is a biometric device attached to or installed
on container 220. Biometric device 222 is a device for gathering
biometric data, such as biometric devices 218. For example,
biometric device 222 may be a device for measuring a customer's
heart rate that is attached to or imbedded within a handle on a
shopping cart. Biometric device 222 may also be a device attached,
coupled to, or associated with any other part or member of a
shopping cart or other shopping container.
Container 220 may also optionally include identification tag 224.
Identification tag 224 is a tag for identifying container 220,
locating container 220 within digital customer marketing
environment 200, either inside or outside retail facility 202,
and/or associating container 220 with a particular customer. For
example, identification tag 224 may be a radio frequency
identification (RFID) tag, a universal product code (UPC) tag, a
global positioning system (GPS) tag, and/or any other type of
identification tag for identifying, locating, and/or tracking a
container.
Container 220 may also include display device 226 coupled to,
mounted on, attached to, or imbedded within container 220. Display
device 226 is a multimedia display device for displaying textual,
graphical, video, and/or audio marketing messages to a customer.
For example, display device 226 may be a digital display screen or
personal digital assistant attached to a handle, front, back, or
side member of container 220.
Display device 226 may be operatively connected to a data
processing system, such as data processing system 100 connected to
digital customer marketing environment 114 in FIG. 1 via wireless,
infrared, radio, or other connection technologies known in the art,
for the purpose of transferring data to be displayed on display
device 226. The data processing system includes the analysis server
for analyzing dynamic external customer data obtained from
detectors 204-210 and set of detectors 212, as well as internal
customer data obtained from one or more databases storing data
associated with one or more customers.
Retail items 228 are items of merchandise for sale. Retail items
228 may be displayed on a display shelf (not shown) located in
retail facility 202. Other items of merchandise that may be for
sale, such as, without limitation, food, beverages, shoes,
clothing, household goods, decorative items, or sporting goods, may
be hung from display racks, displayed in cabinets, on shelves, or
in refrigeration units (not shown). Any other type of merchandise
display arrangement known in the retail trade may also be used in
accordance with the illustrative embodiments.
For example, display shelves or racks may include, in addition to
retail items 228, various advertising displays, images, or
postings. A multimedia display device attached to a data processing
system may also be included. The images shown on the multimedia
display may be changed in real time in response to various events
such as the time of day, the day of the week, a particular customer
approaching the shelves or rack, or items already placed inside
container 220 by the customer.
Retail items 228 may be viewed or identified using an image capture
device, such as a camera or other detector in set of detectors 212.
To facilitate such viewing, an item may have attached
identification tags 230. Identification tags 230 are tags
associated with one or more retail items for identifying the item
and/or location of the item. For example, identification tags 230
may be, without limitation, a bar code pattern, such as a universal
product code (UPC) or European article number (EAN), a radio
frequency identification (RFID) tag, or other optical
identification tag, depending on the capabilities of the image
capture device and associated data processing system to process the
information and make an identification of retail items 228. In some
embodiments, an optical identification may be attached to more than
one side of a given item.
The data processing system, discussed in greater detail in FIG. 3
below, includes associated memory which may be an integral part,
such as the operating memory, of the data processing system or
externally accessible memory. Software for tracking objects may
reside in the memory and run on the processor. The software is
capable of tracking retail items 228, as a customer removes an item
in retail items 228 from its display position and places the item
into container 220. Likewise, the tracking software can track items
which are being removed from container 220 and placed elsewhere in
the retail store, whether placed back in their original display
position or anywhere else including into another container. The
tracking software can also track the position of container 220 and
the customer.
The software can track retail items 228 by using data from one or
more of detectors 204-210 located externally to retail facility
202, internal data captured by one or more detectors in set of
detectors 212 located internally to retail facility 202, such as
identification data received from identification tags 230 and/or
identification data received from identification tags 224.
The software in the data processing system keeps a list of which
items have been placed in each shopping container, such as
container 220. The list is stored in a database. The database may
be any type of database such as a spreadsheet, relational database,
hierarchical database or the like. The database may be stored in
the operating memory of the data processing system, externally on a
secondary data storage device, locally on a recordable medium such
as a hard drive, floppy drive, CD ROM, DVD device, remotely on a
storage area network, such as storage area network 108 in FIG. 1,
or in any other type of storage device.
The lists of items in container 220 are updated frequently enough
to maintain a dynamic, accurate, real time listing of the contents
of each container as customers add and remove items from
containers, such as container 220. The listings of items in
containers are also made available to whatever inventory system is
used in retail facility 202. Such listings represent an
up-to-the-minute view of which items are still available for sale,
for example, to on-line shopping customers or customers physically
located at retail facility 202. The listings may also provide a
demand side trigger back to the supplier of each item. In other
words, the listing of items in customer shopping containers can be
used to update inventories, determine current stock available for
sale to customers, and/or identification of items that need to be
restocked or replenished.
At any time, the customer using container 220 may request to see a
listing of the contents of container 220 by entering a query at a
user interface to the data processing system. The user interface
may be available at a kiosk, computer, personal digital assistant,
or other computing device connected to the data processing system
via a network connection. The user interface may also be coupled to
a display device, such as, at a display device in display devices
214, display devices 216, or display device 226 associated with
container 220. The customer may also make such a query after
leaving the retail store. For example, a query may be made using a
portable device or a home computer workstation.
The listing is then displayed at a location where it may be viewed
by the customer, such as on a display device in display devices 214
inside retail facility 202, display devices 216 outside retail
facility 202, or display device 226 associated with container 220.
The listing may include the quantity of each item in container 220,
as well as the price for each, a discount or amount saved off the
regular price of each item, and a total price for all items in
container 220. Other data may also be displayed as part of the
listing, such as, additional incentives to purchase one or more
other items available in digital customer marketing environment
200.
When the customer is finished shopping, the customer may proceed to
a point-of-sale checkout station. In one embodiment, the checkout
station may be coupled to the data processing system. Therefore,
the items in container 220 are already known to the data processing
system due to the dynamic listing of items in container 220 that is
maintained as the customer shops in digital customer marketing
environment 200. Thus, there is no need for an employee, customer,
or other person to scan each item in container 220 to complete the
purchase of each item, as is commonly done today. In this example,
the customer merely arranges for payment of the total, for example
by use of a smart card, credit card, debit card, cash, or other
payment method. In some embodiments, it may not be necessary to
empty container 220 at the retail facility at all, for example, if
container 220 is a minimal cost item which can be kept by the
customer.
In other embodiments, container 220 may belong to the customer. In
this example, the customer brings container 220 to retail facility
202 at the start of the shopping session. In another embodiment,
container 220 belongs to retail facility 202 and must be returned
before the customer leaves the parking lot or at some other
designated time or place.
In another example, when the customer is finished shopping, the
customer may complete checkout either in-aisle or from a final or
terminal-based checkout position in the store using a transactional
device which may be integral with container 220 or associated
temporarily to container 220. The customer may also complete the
transaction using a consumer owned computing device, such as a
laptop, cellular telephone, or personal digital assistant that is
connected to the data processing system via a network
connection.
The customer may also make payment by swiping a magnetic strip on a
card, using any known or available radio frequency identification
(RFID) enabled payment device. The transactional device may also be
a portable device such as a laptop computer, palm device, or any
other portable device specially configured for such in-aisle
checkout service, whether integral with container 220 or separately
operable. In this example, the transactional device connects to the
data processing system via a network connection to complete the
purchase transaction at check out time.
Checkout may be performed in-aisle or at the end of the shopping
trip whether from any point or from a specified point of
transaction. The customer may also make payment by swiping a
magnetic strip on a card, using a radio frequency identification
(RFID) enabled payment device with the transactional device, using
any biometric type of payment tender via biometric device 218
and/or biometric device 222 known in the art, and/or via any other
known or available method for making a payment or concluding a
transaction.
As noted above, checkout transactional devices may be stationary
shared devices or portable or mobile devices offered to the
customer from the store or may be devices brought to the store by
the customer, which are compatible with the data processing system
and software residing on the data processing system.
Thus, in this depicted example, when a customer enters digital
customer marketing environment but before the customer enters
retail facility 202, such as a retail store, the customer is
detected and identified by one or more detectors in detectors
204-210 to generate external data. If the customer takes a shopping
container before entering retail facility 202, the shopping
container is also identified. In some embodiments, the customer may
be identified through identification of the container.
The customer is tracked using image data and/or other detection
data captured by detectors 204-210 as the customer enters retail
facility 202. The customer is identified and tracked inside retail
facility 202 by one or more detectors inside the facility, such as
set of detectors 212. When the customer takes a shopping container,
such as container 220, the analysis server uses data from set of
detectors 212, such as, identification data from identification
tags 230 and 224, to track container 220 and items selected by the
customer and placed in container 220.
As a result, an item selected by the customer, for example, as the
customer removes the item from its stationary position on a store
display, is identified. The selected item may be traced visually by
a camera, tracked by another type of detector in set of detectors
212 and/or using identification data from identification tags 230.
The item is tracked until the customer places it in container 220
to form a selected item.
Thus, a selected item is identified when a customer removes an item
from a store display, such as a shelf, display counter, basket, or
hanger. In another embodiment, the selected item is identified when
the customer places the item in the customer's shopping basket,
shopping bag, or shopping cart. The analysis server then selects
one or more upsale items related to the selected items for
marketing to the customer. In another embodiment, the analysis
server selects one or more cross-sale items correlated to the
selected item.
The analysis server stores a listing of selected items placed in
the shopping container. The analysis server also stores a listing
of upsale items and/or correlated cross-sale items that are
marketed to the customer and a listing of actually purchased upsale
items and/or correlated cross-sale items that are actually
purchased.
In this example, a single container and a single customer is
described. However, the aspects of the illustrative embodiments may
also be used to track multiple containers and multiple customers
simultaneously. In this case, the analysis server will store a
separate listing of selected items for each active customer. As
noted above, the listings may be stored in a database. The listing
of items in a given container is displayed to a customer, employee,
agent, or other customer in response to a query. The listing may be
displayed to a customer at any time, either while actively
shopping, during check-out, or after the customer leaves retail
facility 202.
Thus, in one embodiment, a customer entering retail facility 202 is
detected by one or more detectors in detectors 204-210. The
customer may be identified by the one or more detectors. An
analysis server in a data processing system associated with retail
facility 202 begins performing data mining on available static
customer data, such as, but not limited to, customer profile
information and demographic information, for use in generating
customized marketing messages targeted to the customer.
In one embodiment, the customer is presented with customized
digital marketing messages on one or more display devices in
display devices 216 located externally to retail facility 202
before the customer enters retail facility 202. When the customer
enters retail facility 202, the customer is typically offered,
provided, or permitted to take shopping container 220 for use
during shopping. Container 220 may contain a digital media display,
such as display device 226, mounted on container 220 and/or
customer may be offered a handheld digital media display device,
such as a display device in display devices 214. In the
alternative, the customer may be encouraged to use strategically
placed kiosks running digital media marketing messages throughout
retail facility 202. Display device 226, 214, and/or 216 may
include a verification device for verifying an identity of the
customer.
For example, display device 214 may include a radio frequency
identification tag reader 232 for reading a radio frequency
identification tag, a smart card reader for reading a smart card,
or a card reader for reading a specialized store loyalty or
frequent customer card. Once the customer has been verified, the
data processing system retrieves past purchase history, total
potential wallet-share, shopper segmentation information, customer
profile data, granular demographic data for the customer, and/or
any other available customer data elements using known or available
data retrieval and/or data mining techniques. These customer data
elements are analyzed using at least one data model to determine
appropriate digital media content to be pushed, on-demand,
throughout the store to customers viewing display devices 214, 216,
and/or display device 226.
The customer is provided with incentives to use display devices
214, 216, and/or display device 226 to obtain marketing incentives,
promotional offers, and discounts for upsale items and/or
cross-sale items correlated to one or more selected items. When the
customer has finished shopping, the customer may be provided with a
list of savings or "tiered" accounting of savings over the regular
price of purchased items if a display device had not been used to
view and use customized digital marketing messages.
This process provides an intelligent guided selling methodology to
optimize customer throughput in the store, thereby maximizing or
optimizing total retail content and/or retail sales, profit, and/or
revenue for retail facility 202. It will be appreciated by one
skilled in the art that the words "optimize", "optimizating" and
related terms are terms of art that refer to improvements in speed
and/or efficiency of a computer implemented method or computer
program, and do not purport to indicate that a computer implemented
method or computer program has achieved, or is capable of
achieving, an "optimal" or perfectly speedy/perfectly efficient
state.
Next, FIG. 3 is a block diagram of a data processing system in
which illustrative embodiments may be implemented. Data processing
system 300 is an example of a computer, such as server 104 or
client 110 in FIG. 1, in which computer usable code or instructions
implementing the processes may be located for the illustrative
embodiments.
In this example, data is transmitted from data processing system
300 to the retail facility over a network, such as network 102 in
FIG. 1. In another embodiment, data processing system 300 is
located on-site at the retail facility.
In the depicted example, data processing system 300 employs a hub
architecture including a north bridge and memory controller hub
(MCH) 302 and a south bridge and input/output (I/O) controller hub
(ICH) 304. Processing unit 306, main memory 308, and graphics
processor 310 are coupled to north bridge and memory controller hub
302. Processing unit 306 may contain one or more processors and
even may be implemented using one or more heterogeneous processor
systems. Graphics processor 310 may be coupled to the MCH through
an accelerated graphics port (AGP), for example.
In the depicted example, local area network (LAN) adapter 312 is
coupled to south bridge and I/O controller hub 304 and audio
adapter 316, keyboard and mouse adapter 320, modem 322, read only
memory (ROM) 324, universal serial bus (USB) ports and other
communications ports 332, and PCI/PCIe devices 334 are coupled to
south bridge and I/O controller hub 304 through bus 338, and hard
disk drive (HDD) 326 and CD-ROM drive 330 are coupled to south
bridge and I/O controller hub 304 through bus 340. PCI/PCIe devices
may include, for example, Ethernet adapters, add-in cards, and PC
cards for notebook computers. PCI uses a card bus controller, while
PCIe does not. ROM 324 may be, for example, a flash binary
input/output system (BIOS). Hard disk drive 326 and CD-ROM drive
330 may use, for example, an integrated drive electronics (IDE) or
serial advanced technology attachment (SATA) interface. A super I/O
(SIO) device 336 may be coupled to south bridge and I/O controller
hub 304.
An operating system runs on processing unit 306 and coordinates and
provides control of various components within data processing
system 300 in FIG. 3. The operating system may be a commercially
available operating system such as Microsoft Windows.RTM. XP
(Microsoft and Windows are trademarks of Microsoft Corporation in
the United States, other countries, or both). An object oriented
programming system, such as the Java programming system, may run in
conjunction with the operating system and provides calls to the
operating system from Java programs or applications executing on
data processing system 300. Java and all Java-based trademarks are
trademarks of Sun Microsystems, Inc. in the United States, other
countries, or both.
Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on
storage devices, such as hard disk drive 326, and may be loaded
into main memory 308 for execution by processing unit 306. The
processes of the illustrative embodiments may be performed by
processing unit 306 using computer implemented instructions, which
may be located in a memory such as, for example, main memory 308,
read only memory 324, or in one or more peripheral devices.
In some illustrative examples, data processing system 300 may be a
personal digital assistant (PDA), which is generally configured
with flash memory to provide non-volatile memory for storing
operating system files and/or customer-generated data. A bus system
may be comprised of one or more buses, such as a system bus, an I/O
bus and a PCI bus. Of course the bus system may be implemented
using any type of communications fabric or architecture that
provides for a transfer of data between different components or
devices attached to the fabric or architecture. A communications
unit may include one or more devices used to transmit and receive
data, such as a modem or a network adapter. A memory may be, for
example, main memory 308 or a cache such as found in north bridge
and memory controller hub 302. A processing unit may include one or
more processors or CPUs.
With reference now to FIG. 4, a diagram of a display device in the
form of a personal digital assistant (PDA) is depicted in
accordance with a preferred embodiment of the present invention.
Personal digital assistant 400 includes a display screen 402 for
presenting textual and graphical information. Display screen 402
may be a known display device, such as a liquid crystal display
(LCD) device. The display may be used to present a map or
directions, calendar information, a telephone directory, or an
electronic mail message. In these examples, display screen 402 may
receive customer input using an input device such as, for example,
stylus 410.
Personal digital assistant 400 may also include keypad 404, speaker
406, and antenna 408. Keypad 404 may be used to receive customer
input in addition to using display screen 402. Speaker 406 provides
a mechanism for audio output, such as presentation of an audio
file. Antenna 408 provides a mechanism used in establishing a
wireless communications link between personal digital assistant 400
and a network, such as network 102 in FIG. 1. Personal digital
assistant 400 also preferably includes a graphical user interface
that may be implemented by means of systems software residing in
computer readable media in operation within personal digital
assistant 400.
Turning now to FIG. 5, a block diagram of a personal digital
assistant display device is shown in accordance with a preferred
embodiment of the present invention. Personal digital assistant 500
is an example of a personal digital assistant, such as personal
digital assistant 400 in FIG. 4, in which code or instructions
implementing the processes of the present invention for displaying
customized digital marketing messages may be located. Personal
digital assistant 500 includes a bus 502 to which processor 504 and
main memory 506 are connected. Display adapter 508, keypad adapter
510, storage 512, and audio adapter 514 also are connected to bus
502. Cradle link 516 provides a mechanism to connect personal
digital assistant 500 to a cradle used in synchronizing data in
personal digital assistant 500 with another data processing system.
Further, display adapter 508 also includes a mechanism to receive
customer input from a stylus when a touch screen display is
employed.
An operating system runs on processor 504 and is used to coordinate
and provide control of various components within personal digital
assistant 500 in FIG. 5. The operating system may be, for example,
a commercially available operating system such as Windows CE, which
is available from Microsoft Corporation. Instructions for the
operating system and applications or programs are located on
storage devices, such as storage 512, and may be loaded into main
memory 506 for execution by processor 504.
The depicted examples in FIGS. 1-5 are not meant to imply
architectural limitations. The hardware in FIGS. 1-5 may vary
depending on the implementation. Other internal hardware or
peripheral devices, such as flash memory, equivalent non-volatile
memory, or optical disk drives and the like, may be used in
addition to or in place of the hardware depicted in FIGS. 1-5.
Also, the processes of the illustrative embodiments may be applied
to a multiprocessor data processing system.
Referring now to FIG. 6, a block diagram of a data processing
system for analyzing biometric data for use in generating
customized marketing messages that promote upsale and cross-sale of
items is shown in accordance with an illustrative embodiment. Data
processing system 600 is a data processing system, such as data
processing system 100 in FIG. 1 and/or data processing system 300
in FIG. 3. Analysis server 602 may be a server, such as server 104
in FIG. 1 or data processing system 300 in FIG. 3. Analysis server
602 includes set of data models 604 for analyzing dynamic customer
data elements and static customer data elements.
Set of data models 604 is one or more data models created a priori
or pre-generated for use in analyzing biometric data and customer
data objects for personalizing content of marketing messages
presented to the customer. Set of data models 604 includes one or
more data models for identifying customer data objects and
determining relationships between the customer data objects. The
data models in set of data models 604 are generated using at least
one of a statistical method, a data mining method, a causal model,
a mathematical model, a marketing model, a behavioral model, a
psychological model, a sociological model, or a simulation
model.
Profile data 606 is data describing one or more customers. In this
example, profile data 606 includes point of contact data, profiled
past data, current actions data, transactional history data,
certain click-stream data, granular demographics 608, psychographic
data 610, customer provided registration data, account data and/or
any other static customer data.
Point of contact data is data regarding a method or device used by
a customer to interact with a data processing system associated
with a retail facility and/or receive customized marketing messages
for display. The customer interacts with the data processing system
using a computing device or display terminal having a user
interface for inputting data and/or receiving output. The device or
terminal may be implemented as display device 632 provided by the
retail facility and/or a device belonging to or provided by the
customer.
If display device 632 is a display device associated with the
retail facility, details and information regarding display device
632 will be known to analysis server 602. However, if display
device 632 is a display device belonging to the customer or brought
to the retail facility by the customer, analysis server 602 may
identify the type of display device using techniques such as
interrogation commands, cookies, or any other known or equivalent
technique. From the type of device other constraints may be
determined such as display size, resolution, refresh rate, color
capability, keyboard entry capability, other entry capability such
as pointer or mouse, speech recognition and response, language
constraints, and any other fingertip touch point constraints and
assumptions about customer state of the display device. For
example, someone using a cellular phone may have a limited time
window for making phone calls and be sensitive to location and
local time of day, whereas a casual home browser may have a greater
luxury of time and faster connectivity.
An indication of a location for the point of contact may also be
determined. For example, global positioning system (GPS)
coordinates of the customer may be determined if the customer
device has such a capability whether by including a real time
global positioning system receiver or by periodically storing
global positioning system coordinates entered by some other method.
Other location indications may also be determined such as post
office address, street or crossroad coordinates, latitude-longitude
coordinates or any other location indicating system.
Analysis server 602 may also determine the connectivity associated
with the customer's point of contact. For example, the customer may
be connected to the merchant or supplier in any of a number ways
such as a modem, digital modem, network, wireless network,
Ethernet, intranet, or high speed lines including fiber optic
lines. Each way of connection imposes constraints of speed,
latency, and/or mobility which can then also be determined.
The profiled past comprises data that may be used, in whole or in
part, for individualization of customized marketing message 630.
Global profile data may be retrieved from a file, database, data
warehouse, or any other data storage device. Multiple storage
devices and software may also be used to store profile data 606.
Some or all of the data may be retrieved from the point of contact
device, as well. The profiled past may comprise an imposed profile,
global profile, individual profile, and demographic profile. The
profiles may be combined or layered to define the customer for
specific promotions and marketing offers.
In the illustrative embodiments, a global profile includes data on
the customer's interests, preferences, and affiliations. The
profiled past may also comprise retrieving purchased data. Various
firms provide data for purchase which is grouped or keyed to
presenting a lifestyle or life stage view of customers by block or
group or some other baseline parameter. The purchased data presents
a view of one or more customers based on aggregation of data points
such as, but not limited to geographic block, age of head of
household, income level, number of children, education level,
ethnicity, and purchasing patterns.
The profiled past may also include navigational data relating to
the path the customer used to arrive at a web page which indicates
where the customer came from or the path the customer followed to
link to the merchant or supplier's web page. Transactional data of
actions taken is data regarding a transaction. For example,
transaction data may include data regarding whether the transaction
is a first time transaction or a repeat transaction, and/or how
much the customer usually spends. Information on how much a
customer generally spends during a given transaction may be
referred to as basket share. Data voluntarily submitted by the
customer in responding to questions or a survey may also be
included in the profiled past.
Current actions, also called a current and historical record, are
also included in profile data 606. Current actions is data defining
customer behavior, such as listings of the purchases made by the
customer, payments and returns made by the customer, and/or
click-stream data from a point of contact device of the customer.
Click-stream data is data regarding a customer's navigation of an
online web page of the merchant or supplier. Click-stream data may
include page hits, sequence of hits, duration of page views,
response to advertisements, transactions made, and conversion
rates. Conversion rate is the number of times the customer takes
action divided by the number of times an opportunity is
presented.
In this example, profiled past data for a given customer is stored
in analysis server 602. However, in accordance with the
illustrative embodiments, profiled past data may also be stored in
any local or remote data storage device, including, but not limited
to, a device such as storage area network 108 in FIG. 1 or read
only memory (ROM) 324 and/or compact disk read only memory (CD-ROM)
330 in FIG. 3.
Granular demographics 608 are a source of static customer data
elements. Static customer data elements are data elements that do
not tend to change in real time, such as a customer's name, date of
birth, and address. Granular demographics 608 provides a detailed
demographics profile for one or more customers. Granular
demographics 608 may include, without limitation, ethnicity, block
group, lifestyle, life stage, income, and education data. Granular
demographics 608 may be used as an additional layer of profile data
606 associated with a customer.
Psychographic data 610 refers to an attitude profile of the
customer. Examples of attitude profiles include, without
limitation, a trend buyer, a time-strapped person who prefers to
purchase a complete outfit, a cost-conscious shopper, a customer
that prefers to buy in bulk, or a professional buyer who prefers to
mix and match individual items from various suppliers.
Dynamic data 612 is data that includes dynamic customer data
elements that are changing in real-time. For example, dynamic
customer data elements could include, without limitation, the
current contents of a customer's shopping basket, the time of day,
the day of the week, whether it is the customer's birthday or other
holiday observed by the customer, customer's responses to marketing
messages and/or items viewed by the customer, customer location,
the customer's current shopping companions, the speed or pace at
which the customer is walking through the retail facility, and/or
any other dynamically changing customer information. Dynamic data
612 also includes external data, grouping data, customer
identification data, customer behavior data, and/or current events
data.
Dynamic data 612 is processed and/or analyzed to generate
customized marketing messages and/or for utilization in selecting
upsale and/or cross-sale items to be marketed to the customer.
Processing dynamic data 612 includes, but is not limited to,
filtering dynamic data 612 for relevant data elements, combining
dynamic data 612 with other dynamic customer data elements,
comparing dynamic data 612 to baseline or comparison models for
external data, and/or formatting dynamic data 612 for utilization
and/or analysis in one or more data models in set of data models
604. The processed dynamic data 612 is analyzed and/or further
processed using one or more data models in set of data models
604.
Correlated items list 614 is a list of one or more items that
provides a different basic functionality than an item selected by
the customer for purchase. The items in the list of correlated
items are items that are different than selected item 620. Selected
item 620 is an identification of an item selected by a customer. An
item is identified as selected item 620 when a customer looks at an
item, reaches for an item, touches an item, picks up an item,
places the item in a shopping container, such as container 220 in
FIG. 2, places the item at a point of sale counter, purchases the
item, indicates an interest in purchasing the item, makes a query
regarding the item, requests information regarding the item, asks
the merchant or sales person questions regarding the item, asks the
merchant or sales person to see the item, or otherwise signals an
intention to purchase the item.
An item is identified as selected item 620 by analyzing video
images of a customer selecting the item, analyzing radio frequency
identification tag data captured by a radio frequency
identification tag reader, analyzing motion sensor data and/or
pressure sensor data from pressure sensors in contact with the
item, and/or any other data associated with the customer, the
customer's movements and behavior, and the location of the item
being selected.
The items in the list of correlated items are items that are
frequently purchased in conjunction with selected item 620. For
example, if a customer selects hot dog buns, hot dogs are
frequently purchased in conjunction with the hot dog buns by a
significant percentage of customers.
Analysis server 602 generates a list of correlated items by
identifying a plurality of items purchased by a set of two or more
customers. The plurality of items are identified using past
purchasing histories for customers, sales records, customer
profiles, customer behavior data, and/or data describing items
purchased by customers during a single shopping trip. Analysis
server 602 analyzes the plurality of items using a set of
correlation techniques to identify items that are typically
purchased in correlation with one or more other items providing a
different basic functionality to form correlated items list
614.
List of correlated items 614 is stored in data storage device 616.
Data storage device 616 is any type of data storage device, such as
storage 108 in FIG. 1. Data storage device 616 may be located
locally to analysis server 602 or remotely to analysis server 602.
Data storage device 616 may be implemented as a single data storage
device or as multiple data storage devices.
Upsale items list 618 is a list of items that provide the same
basic functionality as one or more selected items. An upsale item
may be a different size than a size of selected item 620, a
different brand than a brand of selected item 620, a different
price than a price of selected item 620, or a different packaging
than a packaging of selected item 620. Upsale items may also
provide an additional feature or functionality than selected item
620. Upsale items produce a greater amount of profit or revenue
than a sale of the selected item. In other words, a sale of at
least one upsale item produces a greater amount of revenue or a
greater amount of profit than a sale of selected item 620. In
addition, users of the system can choose to utilize the process to
increase profit even if revenue remains the same or decreases. In
another embodiment, the process is used to increase both profit and
revenue.
In this example, analysis server 602 also uses dynamic data 612 to
select a set of one or more upsale items from upsale items list
618. Dynamic data 612 is used to select at least one upsale item in
upsale items list 618 that is most likely to be purchased by the
customer to form the set of promoted upsale items.
Likewise, analysis server 602 also uses dynamic data 612 to select
a set of one or more cross-sale items from correlated items list
614. Dynamic data 612 is used to select at least one cross-sale
item in correlated items list 614 that is most likely to be
purchased by the customer to form a set of promoted cross-sale
items.
List of correlated items 614 and/or upsale items list 618 may be
pre-generated or generated dynamically as the customer is shopping.
In another example, list of correlated items 614 and/or upsale
items list 618 are generated by a different analysis server than
analysis server 602. In this example, the different analysis server
stores a list of correlated items 614 and/or upsale items list 618
in data storage device 616 for retrieval by analysis server
602.
Biometric data 622 is data regarding a customer's physical
responses. For example, biometric data may include data regarding a
customer's heart rate over a period of time, a fingerprint, a
retinal pattern, a voice stress measurement, a measurement of a
change in pupil dilation as compared to changes in the ambient
light levels, body temperature, a change in skin temperature, a
change in body temperature, a rate or amount of perspiration,
respiratory rate, and/or any other measurement of a customer's
physical traits or physical responses.
Biometric data 622 may be used to determine a customer's response
to an item or marketing message being viewed by a customer at the
time a change in a biometric reading takes place. For example, if a
customer's heart rate or pupil dilation changes while viewing a
marketing message, the change in the heart rate or pupil dilation
may be attributed to the marketing message. Biometric data 622 is
used to identify selected item 620. For example, if a customer's
biometric responses change when the customer is looking at an item
or holding an item, analysis server 602 identifies the item as
selected item 620. Biometric data 622 is also used to generate
customized marketing messages. If a customer's biometric responses
change while the customer is viewing a marketing message, the
customer's responses are used to modify the marketing messages
presented to the user. If biometric data 622 indicates a favorable
response to marketing message elements, those marketing message
elements are used more frequently. If biometric data 622 indicates
a negative response to marketing message elements, those marketing
message elements are used less frequently and/or those elements are
not used or avoided in future marketing messages.
Biometric data such as, without limitation, fingerprint scans,
retinal scans, and voice print analysis may also be used to
dynamically identify a customer while the customer is outside the
retail marketing facility, as well as after the customer has
entered or is inside the retail marketing facility. For example, a
fingerprint scanner on a shopping container or a display device may
be used to determine or verify a customer's identity.
Biometric data 622 is gathered or captured by a set of biometric
devices, such as biometric device 222 and 218 in FIG. 2. The set of
biometric devices includes one or more biometric devices located
within a retail environment, such as digital customer marketing
environment 200 in FIG. 2. The biometric devices in a set of
biometric devices may be located inside a retail facility and/or
outside a retail facility. For example, a biometric device may be
located on a shopping cart that is temporarily located outside the
retail facility. The shopping cart may be moved outside by a
customer leaving the store. The shopping cart may also be found by
a customer arriving at the retail facility. In this case, the
customer may select the shopping cart located outside the retail
facility and take the cart inside the store as the customer enters
the retail facility.
Content server 622 is a server for storing modular marketing
messages 624, such as server 104 in FIG. 1 or data processing
system 300 in FIG. 3. Modular marketing messages 624 are two or
more self contained marketing message components that may be
combined with one or more other modular marketing messages to form
customized marketing message 630. Modular marketing messages 624
can be quickly and dynamically assembled and disseminated to the
customer in real-time.
In this illustrative example, modular marketing messages 624 are
pre-generated, preexisting marketing message units that are created
prior to generating or analyzing dynamic data 612 and/or biometric
data 622 using one or more data models to generate a personalized
marketing message for the customer. Although modular marketing
messages 624 are pre-generated, modular marketing messages 624 may
also include templates imbedded within modular marketing messages
for adding personalized information, such as a customer's name or
address, to the customized marketing message.
Derived marketing messages 626 is a software component for
determining which modular marketing messages in modular marketing
messages 624 should be combined or utilized to dynamically generate
customized marketing message 630 for the customer in real time.
Derived marketing messages 626 uses the output generated by
analysis server 602 as a result of analyzing dynamic data 612
associated with a customer using one or more appropriate data
models in set of data models 604 to identify one or more modular
marketing messages for the customer. The output generated by
analysis server 602 from analyzing dynamic data 612 using
appropriate data models in set of data models 604 includes
marketing message criteria for the customer. Derived marketing
messages 626 uses the marketing message criteria for the customer
to select modular marketing messages 624 for customized marketing
message 630.
A marketing message 630 is a personalized message that presents an
incentive or offer regarding a product or item that is being
marketed, advertised, promoted, and/or offered for sale. A
customized marketing message by presented to a customer visually on
a digital display device and/or in an audio format via speakers or
any other sound system, and/or marketing messages printed out on a
paper medium by a printer. The customized marketing message may
include textual content, graphical content, moving video content,
still images, audio content, and/or any combination of textual,
graphical, moving video, still images, and audio content.
In the illustrative embodiments presented herein, the marketing
messages are messages promoting sales of upsale and cross-sale
items. Customized marketing message 630 is generated using
personalized marketing message criteria, which includes criteria
for selecting one or more modular marketing messages for inclusion
in customized marketing message 630. The personalized marketing
message criteria may include one or more criterion. The
personalized marketing message criteria may be generated, in part,
a priori and in part dynamically in real-time based on the dynamic
data for the customer and/or any available static customer data
associated with the customer.
If an analysis of dynamic data 612 indicates that the customer is
shopping with a large dog, the personal marketing message criteria
may include criteria to indicate marketing of pet food and items
for large dogs. Because people with large dogs often have large
yards, the personal marketing message criteria may also indicate
that yard items, such as yard fertilizer, weed killer, or insect
repellant may should be marketed. The personal marketing message
criteria may also indicate marketing elements designed to appeal to
animal lovers and pet owners, such as incorporating images of
puppies, images of dogs, phrases such as "man's best friend",
"puppy love", advice on pet care and dog health, and/or other pet
friendly images, phrases, and elements to appeal to the customer's
tastes and interests.
Derived marketing messages 626 uses the personalized marketing
message criteria output by one or more data models in set of data
models 604 to identify modular marketing messages to be combined
together to form customized marketing message 630 for the customer.
For example, a first modular marketing message may be a special on
a more expensive brand of peanut butter. A second modular marketing
message may be a discount on jelly when peanut butter is purchased.
In response to marketing message criteria that indicates the
customer frequently purchases cheaper brands of peanut butter, the
customer has children, and the customer is currently in an aisle of
the retail facility that includes jars of peanut butter, derived
marketing messages 626 will select the first marketing message and
the second marketing message based on the marketing message
criteria for the customer.
Dynamic marketing message assembly 628 is a software component for
combining the one or more modular marketing messages selected by
derived marketing messages 626 to form customized marketing message
630. In the example above, after derived marketing messages 626
selects the first modular marketing message and the second modular
marketing message based on the marketing message criteria, dynamic
marketing message assembly 628 combines the first and second
modular marketing messages to generate a customized marketing
message offering the customer a discount on both the peanut butter
and jelly if the customer purchases the more expensive brand of
peanut butter. In this manner, dynamic marketing message assembly
628 provides assembly of customized marketing message 630 based on
output from the data models analyzing dynamic data 612 and/or
biometric data 622 associated with the customer.
Customized marketing message 630 is a customized and unique
marketing message for an upsale item and/or a cross-sale item
associated with selected item 620. The marketing message is a
one-to-one customized marketing message for a specific customer.
Customized marketing message 630 is generated using biometric data
622, dynamic data 612 and/or static customer data elements, such as
the customer's demographics and psychographics, to achieve this
unique one-to-one marketing. Dynamic data 612 may include, without
limitation, grouping data, customer identification data, current
events data, and customer behavior data.
For example, if modular marketing messages 624 include marketing
messages identified by numerals 1-20, customized marketing message
630 may be generated using marketing messages 2, 8, 9, and 19. In
this example, modular marketing messages 2, 8, 9, and 19 are
combined to create a customized marketing message that is generated
for display to the customer rather than displaying the exact same
marketing messages to all customers. Customized marketing message
630 is displayed on display device 632.
Customized marketing message 630 may include advertisements, sales,
special offers, incentives, opportunities, promotional offers,
rebate information and/or rebate offers, discounts, and
opportunities. An opportunity may be a "take action" opportunity,
such as asking the customer to make an immediate purchase, select a
particular item, request a download, provide information, or take
any other type of action. Customized marketing message 630 may also
include content or messages pushing advertisements and
opportunities to effectively and appropriately drive the point of
contact customer to some conclusion or reaction desired by the
merchant.
Customized marketing message 630 is formed in a dynamic closed loop
manner in which the content delivery depends on dynamic data 612,
as well as other dynamic customer data elements and static customer
data, such as profile data 606 and granular demographics 608.
Therefore, all interchanges with the customer may sense and gather
data associated with customer behavior, which is used to generate
customized marketing message 630.
Display device 632 is a multimedia display for presenting
customized marketing messages to one or more customers. Display
device 632 is implemented in a device such as display devices 216,
218, and/or 226 in FIG. 2 or display device 632.
Thus, a merchant has a capability for interacting with the customer
on a direct one-to-one level by sending customized marketing
message 630 to display device 632. Customized marketing message 630
may be sent and displayed to the customer via a network. For
example, customized marketing message 630 may be sent via a web
site accessed as a unique uniform resource location (URL) address
on the World Wide Web, as well as any other networked connectivity
or conventional interaction including, but not limited to, a
telephone, computer terminal, cell phone or print media.
Display device 632 may be a display device mounted on a shopping
cart, a shopping basket, a shelf or compartment in a retail
facility, included in a handheld device carried by the customer, or
mounted on a wall in the retail facility. In response to displaying
customized marketing message 630, a customer can select to print
the customized marketing message 630 as a coupon and/or as a paper
or hard copy for later use. In another embodiment, display device
632 automatically prints customized marketing message 630 for the
customer rather than displaying customized marketing message 630 on
a display screen or in addition to displaying customized marketing
message 630 on the display screen.
In another embodiment, display device 632 provides an option for a
customer to save customized marketing message 630 in an electronic
form for later use. For example, the customer may save customized
marketing message 630 on a hand held display device, on a flash
memory, a customer account in a data base associated with analysis
server 602, or any other data storage device. In this example, when
customized marketing message 630 is displayed to the customer, the
customer is presented with a "use offer now" option and a "save
offer for later use" option. If the customer chooses the "save
offer" option, the customer may save an electronic copy of
customized marketing message 630 and/or print a paper copy of
customized marketing message 630 for later use.
Customized marketing message 630 is generated and delivered to the
customer in response to the customer choosing selected item 620.
Customized marketing message 630 prompts the customer to purchase
an upsale item instead of selected item 620. In another embodiment,
customized marketing message 630 prompts the customer to purchase
one or more correlated cross-sale items in addition to purchasing
selected item 620.
FIG. 7 is a block diagram of a dynamic marketing message assembly
transmitting a customized marketing message to a set of display
devices in accordance with an illustrative embodiment. Dynamic
marketing message assembly 700 is a software component for
combining modular marketing messages into a customized marketing
message for a customer, such as dynamic marketing message assembly
628 in FIG. 6. Dynamic marketing message assembly 700 transmits a
customized marketing message, such as customized marketing message
630 in FIG. 6, to one or more display devices in a set of display
devices.
In this example, the set of display devices includes, but is not
limited to, digital media display device 702, kiosk 704, personal
digital assistant 706, cellular telephone 708, and/or electronic
sign 710. A set of display devices in accordance with the
illustrative embodiments may include any combination of display
devices and any number of each type of display device. For example,
a set of display devices may include, without limitation, six
kiosks, fifty personal digital assistants, and no cellular
telephones. In another example, the set of display devices may
include electronic signs and kiosks but no personal digital
assistants or cellular telephones.
Digital media display device 702 is a device for displaying
content, such as, without limitation, a monitor, a plasma screen, a
liquid crystal display screen, and/or any other type of digital
media display device.
Kiosk 704 is a structure having one or more open sides, such as a
booth. The kiosk includes a computing device associated with a
display screen located inside or in association with the structure.
The computing device may include a user interface for a user to
provide input to the computing device and/or receive output. For
example, the user interface may include, but is not limited to, a
graphical user interface (GUI), a menu-driven interface, a command
line interface, a touch screen, a voice recognition system, an
alphanumeric keypad, and/or any other type of interface.
Personal digital assistant 706 is a device, such as, but not
limited to, personal digital assistant 400 in FIG. 4 and/or
personal digital assistant 500 in FIG. 5. Cellular telephone 708 is
any type of cellular telephone or wireless mobile telephone.
Cellular telephone 708 includes a display screen that is capable of
displaying pictures, graphics, and/or text. Additionally, cellular
telephone 708 may also include an alphanumeric keypad, joystick,
and/or buttons for providing input to cellular telephone 708. The
alphanumeric keypad, joystick, and/or buttons may be used to
initiate various functions in cellular telephone 708. These
functions include for example, activating a menu, displaying a
calendar, receiving a call, initiating a call, displaying a
customized marketing message, saving a customized marketing
message, and/or selecting a saved customized marketing message.
Electronic sign 710 is an electronic messaging system, such as,
without limitation, an outdoor electronic light emitting diode
(LED) display, moving message boards, variable message signs,
tickers, electronic message centers, video boards, and/or any other
type of electronic signage.
A display device may be located externally to the retail facility
to display marketing messages to the customer before the customer
enters the retail facility. In another embodiment, the customized
marketing message is displayed to the customer on a display device
inside the retail facility after the customer enters the retail
facility and begins shopping.
Turning now to FIG. 8, a block diagram of an identification tag
reader for gathering data associated with one or more items is
shown in accordance with an illustrative embodiment. Item 800 is an
item, such as, without limitation, retail items 228 in FIG. 2.
Identification tag 802 associated with item 800 is a tag for
providing information describing item 800 to identification tag
reader 804. Identification tag 802 is a tag such as a tag in
identification tags 230 in FIG. 2. Identification tag 802 may be a
bar code, a radio frequency identification tag, a global
positioning system tag, and/or any other type of tag.
Radio Frequency Identification tags include read-only
identification tags and read-write identification tags. A read-only
identification tag is a tag that generates a signal in response to
receiving an interrogate signal from an item identifier but the tag
does not have a memory. A read-write identification tag is a tag
that responds to write signals by writing data to a memory within
the identification tag. A read-write tag can respond to interrogate
signals by sending a stream of data encoded on a radio frequency
carrier. The stream of data can be large enough to carry multiple
identification codes. In this example, identification tag 802 is a
radio frequency identification tag.
Identification tag reader 804 is a device for retrieving
information from identification tag 802, such as, but is not
limited to, a radio frequency identification tag reader or a bar
code reader. A bar code reader is a device for reading a bar code,
such as a universal product code. Identification tag reader 804 may
be implemented as a tag reader, such as identification tag reader
232 in FIG. 2. In this example, identification tag reader 804
provides identification data 808, item data 810, and/or location
data 812 to an analysis server, such as analysis server 602 in FIG.
6.
Identification data 808 is data regarding the product name and/or
manufacturer name of item 800. Item data 810 is information
regarding item 800, such as, without limitation, the regular price,
sale price, product weight, and/or tare weight for item 800.
Identification data 808 is used to identify a selected item, such
as selected item 620 in FIG. 6. Once the selected item has been
identified, one or more upsale items and/or correlated cross-sale
items are identified for marketing to the customer.
Location data 812 is data regarding a location of item 800 within
the retail facility and/or outside the retail facility. For
example, if identification tag 802 is a bar code, the item
associated with identification tag 802 must be in close physical
proximity to identification tag reader 804 for a bar code scanner
to read a bar code on item 800. Therefore, location data 812 is
data regarding the location of identification tag reader 804
currently reading identification tag 802. However, if
identification tag 802 is a global positioning system tag, a
substantially exact or precise location of item 800 may be obtained
using global positioning system coordinates obtained from the
global positioning system tag.
Identifier database 806 is a database for storing any information
that may be needed by identification tag reader 804 to read
identification tag 802. For example, if identification tag 802 is a
radio frequency identification tag, identification tag will provide
a machine readable identification code in response to a query from
identification tag reader 804. In this case, identifier database
806 stores description pairs that associate the machine readable
codes produced by identification tags with human readable
descriptors. For example, a description pair for the machine
readable identification code "10101010111111" associated with
identification tag 802 would be paired with a human readable item
description of item 800, such as "orange juice." An item
description is a human understandable description of an item. Human
understandable descriptions are for example, text, audio, graphic,
or other representations suited for display or audible output.
FIG. 9 is a block diagram illustrating an external marketing
manager for generating current events data in accordance with an
illustrative embodiment. External marketing manager 900 is a
software component for collecting current news item 902, competitor
marketing data 904, holidays and/or events data 906, and/or any
other current events or news data from a set of sources. The set of
sources may include one or more sources. A source may be, without
limitation, a newspaper, catalog, a web page or other network
resource, a television program or commercial, a flier, a pamphlet,
a book, a booklet, a news board, a coupon board, a news group, a
blog, a magazine, or any other paper or electronic source of
information. A source may also include information provided by a
human user.
External marketing manager 900 stores current news item 902,
competitor marketing data 904, holidays and/or events data 906,
and/or any other current events or news data in data storage device
908 as external marketing data 910. Data storage device 908 may be
implemented as any type of data storage device, including, without
limitation, a hard disk, a database, a main memory, a flash memory,
a random access memory (RAM), a read only memory (ROM), or any
other data storage device.
In this example, external marketing manager 900 filters or
processes external marketing data 910 to form current events data
920. Filtering external marketing data 910 may include selecting
data items or data objects associated with marketing one or more
items to a customer. A data item or data object associated with
marketing one or more items is a data element that may influence a
customer's decision to purchase a product. For example, the
occurrence of a sporting event may influence the sales of beer,
pizza, and large screen televisions. A data element indicating the
occurrence of a holiday, such as Christmas, may influence
purchasing of toys, wrapping paper, candy canes, and other seasonal
items. A data element indicating that it is raining or will rain
all week may influence purchases of umbrellas and rain coats. These
data elements that may influence customer purchases and sales of
items are selected to form current events data 920. Current events
data 920 is then sent to an analysis server, such as analysis
server 602 in FIG. 6 for use in generating customized marketing
messages to a customer.
In this example, external marketing manager 900 filters external
marketing data 910 for relevant data elements to form current
events data 920 without intervention by a human user. In another
embodiment, a human user filters external marketing data 910
manually to generate current events data 920.
The analysis server uses the current events data to identify an
event of interest to the customer that occurs within a
predetermined period of time. For example, if a customer profile
and dynamic data indicates that the customer is a football fan and
current events data 920 indicates that the super bowl is playing on
the upcoming weekend, the analysis server can identify items in a
list of upsale items and items in a list of correlated items that
are associated with the super bowl and football.
For example, items associated with football and the super bowl
might include, without limitation, big screen televisions, beer,
pizza, chips, and dip. These items in the lists of upsale items
and/or list of correlated items that are related to the super bowl
are then marketed in customized marketing messages to the customer
to maximize purchases by the customer.
Referring now to FIG. 10, a block diagram illustrating a smart
detection engine for generating dynamic data is shown in accordance
with an illustrative embodiment. Smart detection system 1000 is a
software architecture for analyzing detection data to form dynamic
data 1020. In this example, the detection data is video images
captured by a camera. However, the detection data may also include,
without limitation, pressure sensor data captured by a set of
pressure sensors, heat sensor data captured by a set of heat
sensors, motion sensor data captured by a set of motion sensors,
audio captured by an audio detection device, such as a microphone,
or any other type of detection data described herein.
Audio/video capture device 1002 is a device for capturing video
images and/or capturing audio. Audio/video capture device 1002 may
be, but is not limited to, a digital video camera, a microphone, a
web camera, or any other device for capturing sound and/or video
images.
Audio data 1004 is data associated with audio captured by
audio/video capture device 1002, such as human voices, vehicle
engine sounds, dog barking, horns, and any other sounds. Audio data
1004 may be a sound file, a media file, or any other form of audio
data. Audio/video capture device 1002 captures audio associated
with a set of one or more customers inside a retail facility and/or
outside a retail facility to form audio data 1004.
Video data 1006 is image data captured by audio/video capture
device 1002. Video data 1006 may be a moving video file, a media
file, a still picture, a set of still pictures, or any other form
of image data. Video data 1006 is video or images associated with a
set of one or more customers inside a retail facility and/or
outside a retail facility.
For example, video data 1006 may include images of a customer's
face, an image of a part or portion of a customer's car, an image
of a license plate on a customer's car, and/or one or more images
showing a customer's behavior. An image showing a customer's
behavior or appearance may show a customer wearing a long coat on a
hot day, a customer walking with two small children which may be
the customer's children or grandchildren, a customer moving in a
hurried or leisurely manner, or any other type of behavior or
appearance attributes of a customer, the customer's companions, or
the customer's vehicle.
Audio/video capture device 1002 transmits audio data 1004 and video
data 1006 to smart detection engine 1008. Audio data 1004 and video
data 1006 may be referred to as detection data. Smart detection
engine 1008 is software for analyzing audio data 1004 and video
data 1006. In this example, smart detection engine 1008 processes
audio data 1004 and video data 1006 into data and metadata to form
dynamic data 1012. Processing the audio data 1004 and video data
1006 may include filtering audio data 1004 and video data 1006 for
relevant data elements, analyzing audio data 1004 and video data
1006 to form metadata describing or categorizing the contents of
audio data 1004 and video data 1006, or combining audio data 1004
and video data 1006 with other audio data, video data, and data
associated with a group of customers received from detectors, such
as detectors 204-210 and set of detectors 212 in FIG. 2.
Smart detection engine 1008 uses computer vision and pattern
recognition technologies to analyze audio data 1004 and/or video
data 1006. Smart detection engine 1008 includes license plate
recognition technology which may be deployed in a parking lot or at
the entrance to a retail facility where the license plate
recognition technology catalogs a license plate of each of the
arriving and departing vehicles in a parking lot associated with
the retail facility.
Smart detection engine 1008 includes behavior analysis technology
to detect and track moving objects and classify the objects into a
number of predefined categories. As used herein, an object may be a
human customer, an item, a container, a shopping cart or shopping
basket, or any other object inside or outside the retail facility.
Behavior analysis technology could be deployed on various cameras
overlooking a parking lot, a perimeter, or inside a facility.
Face detection/recognition technology may be deployed in parking
lots, at entry ways, and/or throughout the retail facility to
capture and recognize faces. Badge reader technology may be
employed to read badges. Radar analytics technology may be employed
to determine the presence of objects. Events from access control
technologies can also be integrated into smart detection engine
1008.
The events from all the above detection technologies are cross
indexed into a single repository, such as multi-mode database. In
such a repository, a simple time range query across the modalities
will extract license plate information, vehicle appearance
information, badge information, and face appearance information,
thus permitting an analyst to easily correlate these
attributes.
Smart detection system 1000 may be implemented using any known or
available software for performing voice analysis, facial
recognition, license plate recognition, and sound analysis. In this
example, smart detection system 1000 is implemented as IBM.RTM.
smart surveillance system (S3) software.
The data gathered from the behavior analysis technology, license
plate recognition technology, face detection/recognition
technology, badge reader technology, radar analytics technology,
and any other video/audio data received from a camera or other
video/audio capture device is received by smart detection engine
1008 for processing into dynamic data 1020. Dynamic data 1020
includes external data 1010, customer identification data 1014,
grouping data 1016, and customer behavior data 1018.
FIG. 11 is a block diagram of a shopping container in accordance
with an illustrative embodiment. Shopping container 1100 is a
container for carrying, moving, or holding items selected by a
customer, such as container 220 in FIG. 2. In this example,
container 1100 is a shopping cart.
Display device 1102 is a multimedia display device for presenting
or displaying customized digital marketing messages to one or more
customers, such as display devices 226 in FIG. 2, personal digital
assistant 400 in FIG. 4, personal digital assistant 500 in FIG. 5,
and/or display device 630 in FIG. 6. In this example, display
device is coupled to shopping container 1100. Display device 1102
displays customized digital marketing messages received from a
derived marketing messages device, such as derived marketing
messages 626 in FIG. 6.
Biometric device 1104 is any type of known or available device for
measuring a physiological response or trait associated with a
customer. Biometric device 1104 is a biometric device, such as,
without limitation, biometric device 222 in FIG. 2. Biometric
device 1104 may be a biometric device for measuring a customer's
heart rate over a given period of time, a change in voice stress
for the customer's voice, a change in blood pressure, and/or a
change in pupil dilation that does not correlate or correspond to a
change in an ambient lighting level.
In this example, biometric device 1104 is coupled to shopping
container 1100. Biometric device 1104 monitors biometric readings
of a customer and detects changes in the biometric readings of the
customer that exceeds a threshold change. In this example,
biometric device 1104 is a device for measuring a customer's heart
rate over time. Biometric device 1104 obtains the customer's pulse
rate by measuring the customer's finger pulse.
In another embodiment, biometric device 1104 may also identify a
customer based on a fingerprint scan, voiceprint analysis, and/or
retinal scan. For example, biometric device 1104 may dynamically
identify the customer by scanning the customer's fingerprint and/or
analyzing fingerprint data associated with the customer to
determine the customer's identity. In one example, biometric device
1104 may, but is not required to, connected to a remote data
storage device storing data to retrieve customer fingerprint data
for use in identifying a given customer using the customer's
fingerprint. Biometric device 1104 may be connected to the remote
data storage device via a wireless network connection, such as
network 102 in FIG. 1.
In this example, biometric device 1104 is coupled, attached, or
imbedded in a handle of shopping container 1100. However, biometric
device 1104 may be coupled, attached, or imbedded in or on any part
or member of shopping container 1100.
In another embodiment, biometric device 1104 is coupled, attached,
associated with, or imbedded within display device 1102. In this
example, display device 1102 may use biometric device 1104 to
dynamically identifying the customer by scanning the customer's
fingerprint and/or analyzing data associated with the customer's
fingerprint to determine the customer's identity.
FIG. 12 is a block diagram of a shelf in a retail facility in
accordance with an illustrative embodiment. Shelf 1200 is any type
of device for showing, displaying, storing, or holding items. Shelf
1200 may be a shelf in a refrigerator or a freezer, as well as a
shelf at room temperature. Shelf 1200 includes biometric sensors
1202-1208 for detecting biometric data associated with a customer.
When a customer is standing in proximity to shelf 1200, such as
when a customer is shopping, browsing, and/or selecting one or more
items for purchase, biometric sensors 1202-1208 monitor biometric
readings associated with the customer, such as, without limitation,
the customer's heart rate, respiration rate, body temperature,
pupil dilation, fingerprint, thumbprint, and/or any other biometric
data. The customer's positive and negative reactions to customized
marketing messages and/or items offered for sale are determined by
analyzing the biometric data gathered by biometric sensors
1202-1208.
Turning now to FIG. 13, a block diagram illustrating a list of
correlated items for promoting cross sales of related items is
depicted in accordance with an illustrative embodiment. Correlated
items list 1300 is a list of selected items 1302 and correlated
items 1304 that provide a different basic functionality than
selected item 1302. Correlated items list 1300 is a list, such as
correlated items list 614 in FIG. 6.
Correlated items list 1300 is generated by analyzing items that are
frequently purchased together by customers. For example, if a
customer purchases peanut butter 1306, it is likely that the
customer will also purchase jelly and/or bread. The correlation
between products is not always a two-way correlation. If a customer
purchases cereal 1308, most of the time, the customer will also
purchase milk. However, customers that select milk for purchase may
not be significantly more likely to purchase cereal.
In some cases, this correlation of different items that are
purchased in conjunction is a two way correlation. For example, if
a customer selects spaghetti pasta 1310, it is very likely that the
customer will also purchase spaghetti sauce. Likewise, if a
customer first selects spaghetti sauce 1312, there is a significant
probability that the customer will also purchase spaghetti
pasta.
In addition, the correlation may be a correlation between a single
selected item 1302 and two or more correlated items. For example,
if a customer selects pizza sauce 1314, there may be a high
likelihood that the customer will also be interested in purchasing
both pizza crust and pizza cheese.
The process identifies an item selected by a customer for purchase
and then uses correlated items list 1300 to identify one or more
correlated items that the customer is most likely to be interested
in purchasing.
FIG. 14 is a block diagram illustrating a list of upsale items
corresponding to selected items in accordance with an illustrative
embodiment. Upsale items list 1400 is a list of items that provide
a same basic functionality as selected item 1402. The upsale items
provide an additional feature or functionality over selected item
1404, such as, but not limited to, a different size, different
ingredients, different method of operation, different method of
replacement or disposal, different packaging, different price than
selected item 1402, or any combination of these features and
functionalities. For example, if a selected item is a six-pack of
root beer 1406, upsale items for the selected item include, without
limitation, a larger twelve-pack size root beer, a twenty-four pack
root beer, a two liter bottle of root beer, or a combination of a
two-litter of root beer and ice cream.
Thus, the upsale item may include a combination of an upsale item
providing a same basic functionality and a correlated item that
provides a different basic functionality. In this case, ice cream
provides a different basic functionality than root beer, but ice
cream may be likely to be purchased by the customer in conjunction
with root beer. Therefore, a marketing message for the upsale item
includes an offer, discount, or incentive for both the upsale item
two liter root beer and the correlated cross-sale item of ice
cream.
The upsale item may be a different size or different number of
items. For example, a sixty count bottle of vitamins 1408 may be
associated with an upsale item of one-hundred count vitamins. The
upsale item may also be a different brand than the selected item.
If the customer selects brand "X" pizza 1410, the upsale item can
be a different brand "Y" pizza 1404.
Referring now to FIG. 15, a flowchart illustrating a process for
generating a customized marketing message for promoting cross sales
of items related to an item selected by a customer is depicted in
accordance with an illustrative embodiment. The process in FIG. 15
is implemented by a server, such as analysis server 602 in FIG.
6.
The process begins by identifying an item selected by a customer
(step 1502). The process retrieves a list of correlated items
related to the selected item (step 1504). The process selects a set
of items in the list of correlated items using biometric data for
the customer to form a set of promoted items (step 1506). In other
words, the process determines whether a customer reacts to
marketing messages and/or items offered for sale and selects items
in the set of items based on the customer's reactions. The process
then generates a customized marketing message for one or more
correlated items in the set of promoted items (step 1508) to
encourage the customer to purchase the correlated items in addition
to purchasing the selected item. The process terminates.
FIG. 16 is a flowchart illustrating a process for generating a list
of items purchased in correlation with a selected item in
accordance with an illustrative embodiment. The process in FIG. 16
is implemented by a server, such as analysis server 602 in FIG.
6.
The process begins by identifying a plurality of items purchased by
a set of one or more customers (step 1602). The process analyzes
the plurality of items using data mining and/or other correlation
analysis techniques to identify correlated items (step 1604). A
correlation analysis technique is any known analysis that compares
items purchased together by customers, identifies items that tend
to be purchased in conjunction with one or more other items, and/or
generates a rate or frequency with which individual items are
purchased in conjunction with one or more other items.
The process stores the correlated items in a data storage device
(step 1606) to form a correlated items list. The process
terminates.
Turning now to FIG. 17, a flowchart illustrating a process for
generating a customized marketing message for promoting upsales of
items is shown in accordance with an illustrative embodiment. The
process in FIG. 17 is implemented by a server, such as analysis
server 602 in FIG. 6.
The process begins by identifying an item selected by a customer
(step 1702). The process retrieves a list of upsale items
associated with the selected item (step 1704). The process selects
a set of items in the list of upsale items using biometric data for
the customer to form the set of promoted items (step 1706). The
process then generates a customized marketing message for an item
in the list of upsale items (step 1706) with the process
terminating thereafter.
Turning now to FIG. 18, a flowchart illustrating a process for
monitoring for a change in biometric readings associated with a
customer is depicted in accordance with an illustrative embodiment.
The process may be implemented by a device for measuring
physiological responses and/or traits of a customer, such as
biometric device 218 and 222 in FIG. 2 and/or biometric device 1104
in FIG. 11.
The process begins by monitoring biometric readings of a customer
obtained from a set of one or more biometric devices (step 1802).
The process makes a determination as to whether a change in the
biometric readings that exceeds a threshold change has been
detected (step 1804). If a change exceeding the threshold is not
detected, the process terminates thereafter.
Returning to step 1804, if a change exceeding the threshold is
detected, the process makes a determination as to whether the
customer was viewing an item, a marketing message, or some other
identifiable person, place, or thing when the change in biometric
readings occurred (step 1806). If the customer was not viewing an
item, a marketing message, or some other identifiable person,
place, or thing, the process terminates thereafter.
Returning to step 1806, if the customer was viewing an item,
marketing message, or something else identifiable, the process
associates the change in biometric reading with the item, the
marketing message, or the identifiable person, place, or thing to
form the biometric data (step 1808). The process transmits the
biometric data to an analysis server and/or stores the biometric
data in a data storage device for later use in generating
customized marketing messages in the future (step 1810) with the
process terminating thereafter.
FIG. 19 is a flowchart illustrating a process for generating a
customized marketing message cross-sales and upsales of items using
dynamic data in accordance with an illustrative embodiment. The
process in FIG. 19 is implemented by a server, such as analysis
server 602 in FIG. 6.
The process begins by making a determination as to whether any
dynamic data for the customer is available (step 1902). Dynamic
data includes, without limitation, external data, grouping data,
customer behavior data, current events data, and/or customer
identification data, and/or vehicle identification data. If dynamic
data is available, the process retrieves the dynamic data (step
1904). The process then retrieves biometric data for the customer
(step 1906). The identification data includes vehicle
identification data.
The process pre-generates or creates in advance, appropriate data
models using at least one of a statistical method, data mining
method, causal model, mathematical model, marketing model,
behavioral model, psychographical model, sociological model,
simulations/modeling techniques, and/or any combination of models,
data mining, statistical methods, simulations and/or modeling
techniques (step 1908).
The process analyzes biometric data with any available dynamic data
using one or more of the appropriate data models to identify a set
of personalized marketing message criteria (step 1910). The set of
personalized marketing message criteria may include one or more
criterion for generating a personalized marketing message. The
process also uses the biometric data and any available dynamic data
to identify correlated items and/or upsale items to form the set of
promoted items (1911).
The process dynamically builds a set of one or more customized
marketing messages for at least one correlated item and/or at least
one upsale item using the personalized marketing message criteria
(step 1912). The process transmits the set of customized marketing
messages to a display device associated with the customer (step
1914) for presentation of the marketing message to the customer,
with the process terminating thereafter.
Thus, the illustrative embodiments provide a computer implemented
method, apparatus, and computer usable program code for generating
customized marketing messages to increase purchases by a customer.
In one embodiment, an item selected by the customer is identified
to form a selected item. Biometric readings for the customer are
received from a set of biometric devices associated with a retail
facility to form the biometric data. The biometric data is data
regarding a set of physiological responses of the customer. A set
of items is selected from a list of items associated with the
selected item using the biometric data for the customer to form a
set of promoted items. A customized marketing message for the
customer is generated using a set of personalized marketing message
criteria for the customer. The customized marketing message
comprises a marketing message for the set of promoted items.
The process permits merchants and retail stores to increase profit
and revenue by increasing the effectiveness of marketing upsale
items and correlated cross-sale items to customers. The customized
marketing message is customized to the customer and the customer's
unique, dynamically changing circumstances at the time the
customized marketing message is presented to the customer. Thus, if
the customer is shopping with children, the customized marketing
messages will be adapted to take advantage of the fact that the
customer may be interested in products for children. In addition,
the customized marketing messages can be generated using imagery,
phrases, jingles, and marketing elements that would appeal to a
parent of small children.
If the biometric data and, optionally, any available dynamic data,
indicates the customer is in a hurry and shopping with children,
upsale and cross sale products for microwaveable meals targeted
towards children are generated. Likewise, shorter marketing
messages are generated to take into account the fact that the
customer appears to be rushed and possibly unwilling to give an
extended amount of attention to a marketing message. In this
manner, profits and revenues are increased by improving marketing
of upsale and cross-sale items to customers.
Biometric data may be used to determine if a user is interested or
disinterested in an item or advertisement based on changes in
biometric data that exceed a threshold or baseline change. For
example, a change in heart rate or pupil dilation may indicate an
interest or desire in a particular product. Also, data such as
voice stress may be used to determine if a customer is receptive to
advertising at the current moment, if the customer is stressed,
tired, or relaxed. This data may be used to customize marketing
messages in real time based on the customer's current mood and
responses to the environment and changing stimuli presented to the
customer to improve marketing effectiveness.
The flowcharts and block diagrams in the different depicted
embodiments illustrate the architecture, functionality, and
operation of some possible implementations of apparatus, methods
and computer program products. In this regard, each step in the
flowchart or block diagrams may represent a module, segment, or
portion of code, which comprises one or more executable
instructions for implementing the specified function or functions.
In some alternative implementations, the function or functions
noted in the step may occur out of the order noted in the figures.
For example, in some cases, two steps shown in succession may be
executed substantially concurrently, or the blocks may sometimes be
executed in the reverse order, depending upon the functionality
involved.
The invention can take the form of an entirely hardware embodiment,
an entirely software embodiment or an embodiment containing both
hardware and software elements. In a preferred embodiment, the
invention is implemented in software, which includes but is not
limited to firmware, resident software, microcode, etc.
Furthermore, the invention can take the form of a computer program
product accessible from a computer-usable or computer-readable
medium providing program code for use by or in connection with a
computer or any instruction execution system. For the purposes of
this description, a computer-usable or computer readable medium can
be any tangible apparatus that can contain, store, communicate,
propagate, or transport the program for use by or in connection
with the instruction execution system, apparatus, or device.
The medium can be an electronic, magnetic, optical,
electromagnetic, infrared, or semiconductor system (or apparatus or
device) or a propagation medium. Examples of a computer-readable
medium include a semiconductor or solid state memory, magnetic
tape, a removable computer diskette, a random access memory (RAM),
a read-only memory (ROM), a rigid magnetic disk and an optical
disk. Current examples of optical disks include compact disk-read
only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
Further, a computer storage medium may contain or store a computer
readable program code such that when the computer readable program
code is executed on a computer, the execution of this computer
readable program code causes the computer to transmit another
computer readable program code over a communications link. This
communications link may use a medium that is, for example without
limitation, physical or wireless.
A data processing system suitable for storing and/or executing
program code will include at least one processor coupled directly
or indirectly to memory elements through a system bus. The memory
elements can include local memory employed during actual execution
of the program code, bulk storage, and cache memories which provide
temporary storage of at least some program code in order to reduce
the number of times code must be retrieved from bulk storage during
execution.
Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the
system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the
data processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modem and Ethernet cards
are just a few of the currently available types of network
adapters.
The description of the present invention has been presented for
purposes of illustration and description, and is not intended to be
exhaustive or limited to the invention in the form disclosed. Many
modifications and variations will be apparent to those of ordinary
skill in the art. The embodiment was chosen and described in order
to best explain the principles of the invention, the practical
application, and to enable others of ordinary skill in the art to
understand the invention for various embodiments with various
modifications as are suited to the particular use contemplated.
* * * * *
References